Parenteral nutrition diagnostic system, apparatus, and method

ABSTRACT

A parenteral nutritional diagnostic system, apparatus, and method are disclosed. In an example embodiment, a parenteral nutritional diagnostic apparatus determines muscle quantity and muscle quality of a patient&#39;s psoas muscle to determine a nutritional status of the patient. An image interface is configured to receive a medical image including radiodensity data related to imaged tissue of the patient. The apparatus also includes a processor configured to use the medical image to determine a tissue surface area for each different value of radiodensity and determine a distribution of the tissue surface area for each radiodensity value. The processor is configured to determine muscle quality by locating a soft tissue peak within the distribution that corresponds to a local peak in at a region related to at least one of muscle tissue, organ tissue, and intramuscular adipose tissue. The processor determines the nutritional status of the patient based on soft tissue peak.

PRIORITY

This application claims priority to and the benefit as a non-provisionalapplication of U.S. Provisional Patent Application No. 62/503,670, filedMay 9, 2017, the entire contents of which is hereby incorporated byreference and relied upon.

BACKGROUND

A body's metabolic response to surgery, cancer treatment, injury,infection, or premature birth oftentimes depends upon the transfer ofamino acids from lean body mass storage locations to sites of activeprotein synthesis. In addition, the toxicity of some chemotherapymedications for cancer treatment can depend on the distribution of themedications between fat and lean body mass tissue. Studies have foundthat deficiencies of total body muscle mass, presumably indicating adeficit in amino acid reserves, may impair the healing supply line,thereby impeding the body's ability to maintain adequate rates ofprotein synthesis in surgical sites or injured areas. Deficiencies ofamino acids can decrease muscle mass, which can impair a body'smetabolic response to surgery, treatment, or injury and lead to longerrecovery times and an increased postoperative (or post-procedural) riskof developing complications, infections, etc. Altogether, thedeficiencies may lead to more hospital return visits, longer hospitalstays, and/or less favorable outcomes.

Currently, clinicians are not capable of quantitatively and objectivelydefining a patient's nutritional status. Oftentimes before a medicalprocedure is performed, a clinician performs an “eyeball test” orsemi-subjective assessment, where a patient's overall nutritional statusis determined from visual observation. For instance, a clinician maypinch certain skin areas to determine dehydration and fat content. Aclinician may also observe how much fat is around a patient's rib cageto gauge malnutrition or starvation or calculate a patient's body massindex (“BMI”) based on weight and height (a patient's weight inkilograms divided by the square of height in meters). In addition to apatient's visual appearance, the clinician may also consult criticalphysiological parameters such as weight, temperature, and heart rate. Insome cases, the clinician may further query a patient regarding how theyare feeling (e.g., self-reported exhaustion) to determine an approximatenutritional status.

Unfortunately, the eyeball test and BMI calculation do not provide amedically conclusive nutritional status of a patient since theseevaluations are heavily influenced by total body fat mass, and cannotaccurately assess muscle mass or muscle quality. Additionally, both ofthe evaluations rely on determining a patient's fat content, not musclequantity or muscle quality because skeletal muscle and connective tissuecannot be easily observed. Both of these known assessments may thereforeprovide a false impression that a patient has an acceptable nutritionalstatus when in fact the patient may have a significant decrease inmuscle mass. Further, the eyeball test is based on the subjectiveevaluation of the clinician and may result in inconsistent applicationamong different clinicians. Another drawback of known evaluation methodsis that under certain time-sensitive circumstances, a clinician may nothave the opportunity to perform the eyeball test or semi-subjectiveassessment before a critical medical procedure is performed.

SUMMARY

The example system, apparatus, and method disclosed herein areconfigured to automatically determine or evaluate internal indicators ofa patient's nutritional status to ascertain whether the patient shouldbe considered for nutritional therapy (e.g., a parenteral nutritiontherapy) prior to or soon after undergoing an intensive medicalprocedure. The example system, apparatus, and method disclosed hereingenerate a measurement of body muscle mass as an indicator of total bodyprotein stores. The muscle mass measurement provides an objective index,value, or indicia that are used to evaluate a patient's individual riskof suffering postoperative complications as a result of a deficiency ofamino acids stores.

The example system, apparatus, and method generate a muscle massmeasurement(s) by analyzing a cross-sectional slice of a patient'sabdomen or mid-section. The cross-sectional slice may comprise atwo-dimensional image recorded by a computed tomography (“CT”) imagingdevice. The image shows, for example, radiodensity levels of tissue. Theexample system, apparatus, and method disclosed herein use theradiodensity levels to determine surface areas of distinguishable tissuetypes including bone tissue, muscle tissue, fat tissue (e.g., visceraladipose tissue and/or subcutaneous adipose tissue), transitional softtissue (e.g., transitional epithelium, intramuscular adipose tissue,muscle tissue infiltrated by fat tissue), and organ tissue. The examplesystem, apparatus, and method determine total cross-sectional areas forthe different tissue types and determine an amount of muscle tissuerelative to fat and transitional soft tissue. The example system,apparatus, and method may identify a patient as likely nutritionallydeficient if the amount of muscle tissue relative to fat or transitionalsoft tissue is below a specified threshold. In some instances, thethreshold may be adjusted based on patient demographics, disease state,and/or physiological parameters. The example system, apparatus, andmethod disclosed herein accordingly provide a diagnostic system toquickly and efficiently determine or evaluate a nutritional status of apatient, which may be used to treat malnourishment prior to or after asurgical procedure or chemotherapy.

In addition to evaluating a nutritional status of a patient, the examplesystem, apparatus, and method disclosed herein are configured todetermine, recommend, or select a parenteral nutritional treatment basedon the amount of muscle tissue relative to fat or transitional softtissue. The example system, apparatus, and method may recommend theparenteral nutritional treatment by selecting nutritional administrationparameters to program a parenteral nutrition pump. In addition, theexample system, apparatus, and method may prepare or recommend thepreparation of a nutritional substance (or select a premixed nutritionalsubstance) based on the amount of muscle tissue relative to fat ortransitional soft tissue, among other information.

In an example embodiment, a parenteral nutritional diagnostic systemincludes a CT imaging device configured to perform a scan on amid-section of a patient and produce a set of two-dimensional imageseach of a slice at a different cross-sectional height of themid-section, each two-dimensional image including radiodensity datarelated to imaged tissue of the patient. The example system alsoincludes a soft tissue analysis server communicatively coupled to the CTimaging device. The soft tissue analysis server is configured to selecta target two-dimensional image among the set of two-dimensional imagesby using the radiodensity data to determine which of the two-dimensionalimages includes a lowest amount of bone tissue and use the targettwo-dimensional image to determine a tissue surface or cross-sectionalarea for each different value or level of radiodensity. The soft tissueanalysis server may additionally create a distribution plot of thetissue surface or cross-sectional area for each radiodensity value inHounsfield Units (“HU”), locate a soft tissue peak within thedistribution plot that corresponds to a local peak in the range of −50HU and 80 HU, and transmit an indication of the soft tissue peak.

The system of the example embodiment further includes a pharmacypreparation system communicatively coupled to the soft tissue analysisserver. The pharmacy preparation system is configured to recommend if aparenteral nutritional treatment is to be performed before a medicalprocedure is to be performed for the patient if the data related to thesoft tissue peak is below a predetermined threshold, recommend anutritional order parameter of the parenteral nutritional treatmentbased at least in part on the data related to the soft tissue peak, andtransmit the recommended nutritional order parameter of the parenteralnutritional treatment. Moreover, the example system includes aparenteral nutrition pump communicatively coupled to the pharmacypreparation system. The parenteral nutrition pump is configured toprogram a parenteral nutrition infusion therapy based on the receivedrecommended nutritional order parameter of the parenteral nutritionaltreatment and provide the parenteral nutrition infusion therapy to thepatient.

In another example embodiment, a parenteral nutritional diagnosticapparatus includes an image interface communicatively coupled to atleast one imaging device. The image interface is configured to receive aset of two-dimensional images each of a slice at a differentcross-sectional of a mid-section of a patient. Each two-dimensionalimage includes radiodensity data related to imaged tissue of thepatient. The example apparatus also includes at least one processorconfigured to select a target two-dimensional image among the set oftwo-dimensional images that corresponds to a desired area (e.g., an areabetween a third lumbar vertebra and a fourth lumbar vertebra) of thepatient. The at least one processor is also configured to use the targettwo-dimensional image to determine a tissue area for each differentlevel or value of radiodensity and determine a distribution of thetissue surface or cross-sectional area for each radiodensity value. Theat least one processor is also configured to locate a soft tissue peakwithin the distribution that corresponds to a local peak in a regionrelated to muscle tissue and intramuscular adipose tissue and determineor recommend a nutritional status of the patient based on soft tissuepeak and potentially other information.

Additional features and advantages of the disclosed system, method, andapparatus are described in, and will be apparent from, the followingDetailed Description and the Figures.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates a diagram of a graph that conceptually illustrates arelationship between muscle quality and postoperative issues.

FIGS. 2 and 3 illustrate diagrams of two-dimensional cross-sectional CTimages that illustrate muscle degradation in a patient.

FIGS. 4 and 5 illustrate diagrams of a hospital environment including ananalysis server configured to determine a nutritional status of apatient and create/administer a nutritional treatment for the patientbased on muscle quality and muscle quantity, according to exampleembodiments of the present disclosure.

FIG. 6 illustrates a diagram of a soft tissue engine included within theanalysis server of FIGS. 4 and 5, according to an example embodiment ofthe present disclosure.

FIG. 7 illustrates a graph that illustrates bone surface area in squarecentimeters in a patient's lumbar region for determining a medical imageto analyze for muscle quality and muscle quantity, according to anexample embodiment of the present disclosure.

FIGS. 8 and 9 illustrate center-of-masses within medical images thatwere determined by the soft tissue engine of FIG. 6, accordingly toexample embodiments of the present disclosure.

FIGS. 10 and 13 illustrate diagrams of target medical images that may beanalyzed by the soft tissue engine of FIG. 6 to determine muscle tissuequantity, accordingly to example embodiments of the present disclosure.

FIGS. 11 and 14 illustrate diagrams of distribution graphs that showtotal tissue pixel counts for each radiodensity value in HU from therespective target medical images of FIGS. 10 and 13, according toexample embodiments of the present disclosure.

FIG. 12 illustrates a diagram representative of muscle quality and/orquantity data that may be determined, stored, and transmitted by thesoft tissue engine of FIG. 6 based on the distribution graphs of FIGS.11 and 14, according to an example embodiment of the present disclosure.

FIG. 15 illustrates a diagram of an example nutritional status recordthat may be created by the soft tissue engine of FIG. 6, according to anexample embodiment of the present disclosure.

FIGS. 16 and 18 illustrate examples of segmentation capable of beingperformed by the soft tissue engine of FIG. 6 on the respective medicalimages of FIGS. 10 and 13, accordingly to example embodiments of thepresent disclosure.

FIGS. 17 and 19 illustrate distribution graphs created by the softtissue engine of FIG. 6 based on the respective segmented medical imagesof FIGS. 16 and 18, accordingly to example embodiments of the presentdisclosure.

FIG. 20 illustrates a diagram of an example medical image that has beensegmented using the center-of-mass approach by the soft tissue engine ofFIG. 6, according to an example embodiment of the present disclosure.

FIG. 21 illustrates a flow diagram representing an example procedure todetermine a nutritional status of a patient from muscle quality andmuscle quantity data obtained from one or more medical images, accordingto an example embodiment of the present disclosure.

FIG. 22 illustrates a diagram of a table illustrating muscle qualityexperimental results using the soft tissue engine of FIG. 6, accordingto an example embodiment of the present disclosure.

FIG. 23 illustrates a diagram of a nutritional analysis engine thatoperates in conjunction with the analysis server of FIGS. 4 and 5,according to an example embodiment of the present disclosure.

FIG. 24 illustrates a diagram representing an example algorithm that isexecutable by the nutritional analysis engine of FIG. 23 to determinewhether an alarm and/or an alert are to be generated based on musclequality data and/or muscle quantity data, according to an exampleembodiment of the present disclosure.

FIG. 25 illustrates a flow diagram showing an example procedure toprogram a nutritional infusion pump based on a patient's nutritionalstatus, which has been determined by the soft tissue engine of FIG. 6,according to an example embodiment of the present disclosure.

DETAILED DESCRIPTION

The example system, apparatus, and method disclosed herein are relatedto diagnostically determining or evaluating a nutritional status of apatient. More particularly, the example system, apparatus, and methodare directed to evaluating muscle quality and muscle quantity from atleast one medical image to assess a patient's post-procedural riskbefore or after undergoing a medical procedure. In some instances, theexample system, apparatus, and method may be used to provide arecommendation that a patient is to receive a nutritional therapy, suchas a parenteral nutritional therapy, before or shortly after beginning amedical procedure. The example system, apparatus, and method may alsoprovide recommendations or be used to determine parameters for thenutritional therapy based on muscle quality and/or muscle quantity data.

Studies have shown that body composition (i.e., the proportion of fatand muscle tissue) is related to risk factors associated with medicalconditions. Bodies that have relatively less muscle tissue are usuallydeficient in protein or amino acid reserves, which are used to fuel abody's response to surgery, injury, medical treatment, or disease. Lowlevels of muscle mass in a body have been found to prolong recovery timeand/or increase complications. In addition, low levels of amino acids,or more generally, muscle quantity, have also been linked to increasesin toxicity from chemotherapy because the reserves determine the volumeof distribution for water-soluble drugs. Lower volumes of muscle massmay cause a standard chemotherapy dose to result in toxic tissue levels.Older patients with sarcopenia, a muscle wasting syndrome that involvesthe loss of muscle tissue, are especially susceptible to postoperativecomplications. Additionally, infants and patients that are malnourished,fragile, or anorexic typically have low amino acid reserves.

FIG. 1 shows a diagram of a graph 100 that illustrates a conceptualrelationship between muscle quality and postoperative issues.Specifically, the graph 100 shows a general relationship between aprobability of a patient developing a postoperative complication andmuscle density (i.e., radiodensity) measured in Hounsfeld Units (“HU”),which are units of radiation attenuation. The graph 100 is based on ageneralization of known studies that have determined a significantcorrelation between muscle density and a probability of a patientdeveloping a complication. Muscle generally has a radiodensity between40 HU and 80 HU. Transitional soft tissue, such as transitionalepithelium, intramuscular adipose, and/or muscle tissue infiltrated byfat tissue has a radiodensity between −50 HU and 40 HU. By comparison,fat (e.g., visceral adipose tissue and/or subcutaneous adipose tissue)has a radiodensity between −190 HU and −50 HU.

The graph 100 shows that a probability of complications increases asmuscle radiodensity decreases. In other words, as muscle tissue (such asthe psoas muscle) degrades or becomes infiltrated with fat, the chancesof postoperative complications dramatically increase. Further, as tissueradiodensity decreases, the amount of storage available for amino acidsdecreases. In contrast, muscle tissue with a radiodensity greater than55 HU (where there is significantly more muscle mass compared to othersoft tissue) is associated with relatively low probabilities of apatient developing a complication.

Unfortunately, there are no known methods to objectively quantify aminoacid reserves or muscle quality by physical examination alone. Asmentioned before, clinicians may perform an eyeball test to gauge apatient's nutritional status. There also exist some preoperative riskstratification tools, such as the American College of Surgeons NationalSurgical Quality Improvement Program (“ACS NSQIP”) surgical calculator.These risk tools help estimate complication rates from various factors.However, the data is estimated based upon information a patient gives tothe healthcare provider about prior health history and does not takeinto account patient-specific measures of vulnerability, frailty, oroverall nutritional status.

In contrast to subjective methods, there exist manual time-consumingobjective methods to perform a body composition analysis. For instance,researchers can manually select a two-dimensional CT image taken ateither the third lumbar vertebra (“L3”) and/or the fourth thoracicvertebra (“T4”). These specific skeletal landmarks have been found tocorrelate well with whole body muscle-to-fat ratios. After selecting theimages, researchers painstakingly segment the muscle and fat tissueregions using available software products such as, for example, theSliceOmatic™ from TomoVision®. The software requires that a usermanually trace a cursor over boundaries of the desired regions, whichhave fairly complex shapes. While relatively accurate, the manualprocess takes roughly 10 to 20 minutes per image. Given the urgency ofsome medical emergencies and the workload of current hospital imagingdepartments, the lengthy time to determine a patient's body compositionusually results in the manual muscle quantification analysis to beskipped or not even considered.

There are also known experimental methods that attempt to automaticallysegment muscle tissue from fat tissue. These methods attempt to overcomeissues in which muscle tissue cannot be distinguished from organ tissueor transitional soft tissue as a result of overlapping radiodensityproperties. As mention above, muscle tissue has a radiodensity between40 and 80 HU while organ tissue has a radiodensity between 30 to 60 HU.The overlap between muscle tissue and organ tissue is due to theinclusion of some muscle tissue within organ tissue. The experimentalmethods attempt to segment between muscle and organ tissue usingstatistical shape-matching, shape-deformation, and/ortemplate-deformation to identify surface boundaries of muscle tissue.However, these known methods use shape modeling and assume that muscleshape is consistent among different patients. While the assumption maybe accurate for healthy patients, the studies show errors formalnourished patients, where degradation in skeletal muscle mass usuallyresults in asymmetric or random changes in the muscle shape (which canbe even more pronounced in a two-dimensional image). The result is thatthe actual muscular shape for malnourished patients may not match thepredefined shapes or templates.

In addition, known studies have focused primarily on segmenting onlymuscle tissue, such as psoas muscle tissue. The studies did notadequately quantify transitional soft tissue or muscle tissueinfiltrated with fat tissue. Transitional soft tissue may be unevenlydistributed around muscle tissue, which makes any type of shape-basedsegmentation difficult, if not impossible. Additionally, muscle tissueinfiltrated with fat tissue may be incorrectly identified as pure muscletissue. Some known studies focused on segmentation between externalboundaries of muscle tissue and do not consider situations in whichinterior portions of the shape may not contain exclusively muscle. Theresult is that some of the known studies may overestimate musclequantity in instances in which muscle tissue has significant fatinfiltration. Accordingly, these known techniques may be inadequateregarding malnourished or fragile patients that have significant muscledegradation or fat infiltration.

FIGS. 2 and 3 show diagrams of two-dimensional cross-sectional CT images200 and 300 that illustrate muscle degradation in a patient. Thetwo-dimensional cross-sectional CT images 200 and 300 were recorded atthe L3 region of a patient with lung cancer. The image 200 was recorded390 days before the patient died. In comparison, FIG. 3 was recorded 58days prior to death. The images 200 and 300 show the patient's backbone202 at L3 in addition to skeletal psoas muscle 204. The images 200 and300 also show visceral adipose tissue 206, subcutaneous adipose tissue208, and intramuscular adipose tissue 210 (muscle infiltrated with fatand/or connective tissue) in addition to internal organs 212, whichthemselves may include muscle tissue or muscle cells. Between the timeimage 200 was recorded and image 300 was recorded, the patientexperienced a decrease in skeletal muscle from 173 cm² to 86.7 cm².During this time, the patient also experienced an increase in the amountof intramuscular adipose tissue 210 and visceral adipose tissue 206.

It should be noted that above-mentioned known studies that use shape ortemplate matching may count the intramuscular adipose tissue 210 asmuscle tissue 204 since at least some of the tissue 210 is within theexternal boundaries of the muscle tissue 204. In other words, thetemplate shapes of solid patterns that assume everything with theboundaries is muscle tissue. The templates do not account for any fattissue interspaced with muscle tissue. Using the known musclesegmentation methods, the examples illustrated in FIGS. 2 and 3 appearto show minor decreases in muscle area. However, when consideringintramuscular adipose tissue 210, the amount of muscle loss isrelatively more pronounced.

In addition, while the known studies discuss the quantification ofmuscle tissue, they are limited in their correlation of an overallnutritional status of a patient. Knowing a patient's muscle quantity isbeneficial but it provides little context without additionalinformation. For example, shorter patients may generally have lessskeletal muscle than taller patients. In another example, older patientsexperiencing sarcopenia naturally have less skeletal muscle compared toyounger patients. The difference in muscle quantity among differentpatients means that, absent another metric, the muscle quantitydetermined by the known studies has to be compared to muscle quantitiesof similar populations of patients to determine if the patient isnutritionally healthy compared to patients with similar demographiccharacteristics.

The example system, apparatus, and method disclosed herein attempt toovercome the above-described limitations of known studies bydifferentiating between intramuscular adipose tissue and muscle tissue(e.g., between the tissue 204 and 210 of FIGS. 2 and 3) to determinemuscle quality. In other words, the example system, apparatus, andmethod not only quantify an amount of muscle in a patient, but alsodetermine a relative muscle quality for that patient. In some instances,the patient's muscle quality may be compared to population data todetermine or evaluate a patient's nutritional status relative to knownnutritional statuses of patients with similar demographiccharacteristics. While the quantification and qualification of skeletalmuscle is discussed throughout, in some instances, the example system,apparatus, and method may quantify and/or qualify connective skeletaltissue.

As described in more detail below, muscle quality is determined as arelation between muscle tissue and intramuscular adipose tissue.Experimentation has shown that there exists a localized soft tissue peakin a Hounsfield distribution of muscle quantity. The location of thesoft tissue peak is related to or indicative of the nutritional statusof the patient. For instance, experiments have demonstrated thatpatients with a muscle mass deficiency have a soft tissue peak that isless than 40 HU (i.e., a peak that is outside the Hounsfield rangeassociated with muscle tissue). By comparison, the experiments havedemonstrated that patients with a normal muscle mass have a soft tissuepeak that is greater than 40 HU (i.e., a peak that is within theHounsfield range associated with muscle tissue). The location of thesoft tissue peak on the Hounsfield distribution provides an indicationof a nutritional status of the patient. In addition, information relatedto the soft tissue peak, such as peak height, standard deviation fromthe peak height, skew of the soft tissue peak, a percentage of pixels orsoft tissue to the right of the soft tissue peak, a ratio of peak heightto muscle height, and/or muscle quantity may provide further informationregarding the nutritional status of the patient. As provided below, insome embodiments, the soft tissue peak and related information may alsobe used to determine or recommend parameters for a nutritional therapyand/or a composition of a nutritional solution.

Certain terms are used throughout this disclosure. As provided herein,nutritional status may refer to an overall nourishment of a patient asdetermined from a quantity and/or quality of muscle in a specifiedregion. Nutritional status indicates, for example, whether a patient hasa normal amount of muscle mass. More generally, nutritional statusindicates whether a patient is malnourished, undernourished, starved, orhealthy. As disclosed herein, the nutritional status of a patient isused as an indicator as to whether a patient has sufficient amino acidreserves (and/or energy reserves) to undergo an intensive medicalprocedure without excessive risk of complications thereafter. Thenutritional status is based upon or otherwise includes a soft tissuepeak value and/or soft tissue peak information. The nutritional statusmay be specified as a numerical score (e.g., from 0 to 100) or texturaldescriptor (e.g., malnourished, starved, etc.) based on a soft peaktissue value and/or soft peak tissue information.

Reference is made throughout to soft tissue, soft tissue peak, and softtissue information. As described below, soft tissue (or transitionaltissue) includes intramuscular adipose tissue, connective tissue, andother types of adipose tissue having a radiodensity between −50 HU and40 HU. Soft tissue generally does not include visceral adipose tissueand subcutaneous adipose tissue, which have radiodensities below −50 HU.

Soft tissue peak may refer to a localized peak within a Hounsfielddistribution that illustrates radiodensity of a defined quantity oftissue within a medical image (e.g., a two-dimensional CT scan image).The peak identifies a radiodensity level of a median value of softtissue including muscle tissue, fat tissue, and intramuscular adiposetissue within a defined area or segmented region of the medical image.In addition, the soft tissue peak is indicative as to whether a majorityor significant portion of a patient's soft tissue comprises muscletissue, intramuscular adipose tissue, or a combination thereof.

Generally, the soft tissue peak is located between −10 HU and 60 HUbased on the health of a patient. Muscle tissue has a radiodensitybetween 40 HU and 80 HU, while intramuscular adipose tissue has aradiodensity between −50 HU and 40 HU. Connective tissue has aradiodensity between 10 HU and 40 HU. Patients that are classified asmalnourished, starving, or frail typically have soft tissue peaksbetween −10 HU and 40 HU, which indicates that a significant portion ofthe muscle has been infiltrated with intramuscular adipose tissue orthere is more connective tissue and adipose tissue compared to muscletissue. In other words, susceptible patients have less muscle mass ormuscle mass that has been replaced by fat (e.g., lower quality muscle),which means that those patients have significantly less amino acidreserves to assist in recovery. In contrast, patients that areclassified as nutritionally normal have soft tissue peaks greater than40 HU, which indicates that the muscle tissue does not contain much, ifany, intramuscular adipose tissue. The relatively higher quality muscletypically contains sufficient amino acid reserves to assist in apatient's recovery.

Soft tissue peak information may refer to data or information that isdeterminable from a soft tissue peak. Generally, a soft tissue peak hasa Gaussian-type distribution on a Hounsfield scale. On a typicaldistribution, a soft tissue peak has a HU value in addition to a muscle(or soft tissue) quantity value (e.g., a number of pixels in a medicalimage that have a specific HU value and/or an area). The soft tissuepeak also has a slope on either side of the peak, which takes intoaccount standard deviations. In some instances, the soft tissue peak maybe skewed towards muscle tissue or fat tissue. Certain information canbe determined from a soft tissue peak, including a HU value associatedwith a center or a peak, HU values associated with first and secondstandard deviations from the peak, and/or muscle or soft tissue quantityassociated with the peak, muscle or soft tissue quantity associated withstandard deviations of the peak. In addition, soft tissue peakinformation may include a ratio of the soft tissue peak to values ofsoft tissue valleys (e.g., fat or thin tails) along the distribution.Further, the soft tissue peak may indicate an amount of skew or musclereach (e.g., a percentage of soft tissue to the right of the peak).

As provided herein, the soft tissue information may be used inconjunction with the soft tissue peak to determine or evaluate anutritional status of a patient. The soft tissue information and softtissue peak may also be used to determine or recommend parameters for aparenteral nutritional therapy and/or contents for a parenteralnutritional solution. For example, a difference between a soft tissuepeak and 40 HU may be used to determine an infusion duration or anamount of amino acids to be added to a parenteral nutritional solution.In some embodiments, the example, system, apparatus, and method may usethe soft tissue peak and related information to determine that patientsidentified as being significantly malnourished are to be prescribedparenteral nutritional therapies having greater durations and are to beprovided solutions that have 25% to 40% more amino acid or proteincontent.

The example system, apparatus, and method are described herein asdetermining a quality and quantity of skeletal muscle. As mentionedabove, skeletal muscle typically has a radiodensity between 40 HU and 80HU. The present disclosure uses the psoas muscles as an examplethroughout because a cross-sectional area of the psoas muscles (or thethoracic muscles) are generally representative of skeletal musclequality in other parts of the body. Since amino acid reserves aretypically located in skeletal muscle and connective tissue, quantifyingpsoas skeletal muscle provides an indication of a patient's overallamino acid reserves (and indicate a patient's postsurgical risk). Whilethe present disclosure focuses on the psoas muscle, it should beappreciated that the example system, apparatus, and method may evaluatemuscle quality and/or quantity of other skeletal muscles including, forexample, triceps muscle, biceps muscle, deltoid muscle, oblique muscle,abdominal muscle, sternum muscle, pectineus muscle, adductor muscle,sartorius muscle, thoracic muscles, etc. It should be furtherappreciated that the example system, apparatus, and method may evaluatemuscle quality and/or quantity for groups of muscles or muscle mass inan entire section of a medical image(s) having validated correlationsbetween patient muscle mass and nutritional status.

It should be appreciated that the example system, apparatus, and methodmay be used to determine or evaluate risks for many types of medicalprocedures. For instance, examples below discuss the use of anutritional status of a patient to determine or evaluate risks forchemotherapy, radiation, and/or traumatic injury treatment. The system,apparatus, and method may also be used for other treatments includingpancreatic cancer therapies, prostate cancer therapies, ovarian orbreast cancer therapies, organ transplants, hip or joint replacementprocedures, gene therapy, blood transfusions, hemodialysis treatments,peritoneal dialysis treatments, etc. Further, the system, apparatus, andmethod may use a patient's nutritional status to treat patientconditions, such as premature birth, anorexia, malnutrition, or disease.Accordingly, the example system, apparatus, and method disclosed hereinmay be incorporated into a patient's treatment plan, medical management,and/or medical workflow to manage post-procedural risks and improve thepatient's outcome.

Medical Environment Embodiments

FIG. 4 illustrates one embodiment of a medical environment 400 of thepresent disclosure configured to determine and/or evaluate a nutritionalstatus of a patient and recommend/create/administer a nutritionaltreatment for the patient. The example environment 400 includes twoprimary components: a nutritional status diagnostic component 402 and anutritional therapy component 404. Both of the components 402 and 404,and more generally, the medical environment 400 may be part of ahospital, a hospital system, a clinic, a doctor's office, an emergencycare facility, etc. In some instances, the components 402 and 404 may bephysically separated. For instance, the nutritional status component 402may be located at an imaging center while the nutritional therapycomponent 404 (or portions of the component 404) is located at ahospital, clinic, or a patient's home.

The example nutritional status diagnostic component 402 is configured todetermine and/or evaluate a nutritional status of a patient from one ormore medical images. The component 402 includes imaging devices 406 aand 406 b (referred to herein collectively as imaging device 406 orgenerally individually as imaging device 406) communicatively coupled toan analysis server 408 via a network 410. While FIG. 4 shows two medicalimaging devices 406 a and 406 b, it should be appreciated that there maybe fewer or additional imaging devices.

The imaging devices 406 are configured to transmit medical images 412 tothe analysis server 408. The images 412 may include, for example,measured radiodensity data associated with each pixel within the image.The analysis server 408 may be configured to use medical images 412 todetermine and/or evaluate a nutritional status of a patient. Thenutritional status is transmitted in one or more message(s) 414 from theanalysis server 408 to the nutritional therapy component 404. In someinstances, the message(s) 414 may be indicative of a soft tissue peakand/or include data related to a determined soft tissue peak includingsoft tissue peak information.

The example nutritional therapy component 404, in one embodiment, isconfigured to determine (or recommend) whether a patient is to beprovided with a nutritional therapy prior to undergoing another medicalprocedure based on the nutritional status determined in the nutritionalstatus diagnostic component 402. The nutritional therapy component 404may also determine parameters for a nutritional pump based, at least inpart, on the determined nutritional status of the patient. Theparameters may be included within a nutritional therapy pumpprescription message 425, which may be electronically transmitted toprogram a nutritional pump 422. The nutritional status diagnosticcomponent 402 may further determine components of a nutritional solution(or recommend a premixed nutritional solution) based, at least in part,on the determined nutritional status of the patient.

As illustrated in FIG. 4, the nutritional therapy component 404 mayinclude at least one pharmacy preparation system 420 and at least onenutritional infusion pump 422. As provided in more detail below, thepharmacy preparation system 420 is configured to, in one embodiment,among other things, prepare a nutritional solution for administration toa patient. The example infusion pump 422 is configured to administer anutritional solution (or any other fluid) to a patient. The pharmacypreparation system 420 and the at least one infusion pump 422 arecommunicatively coupled together via the network 410.

In addition to the pharmacy preparation system 420 and the infusion pump422, the example nutritional therapy component 404 also includes one ormore clinician devices 424 and an electronic medical record (“EMR”)server 426. The clinician devices 424 may include any smartphone, tabletcomputer, workstation (e.g., nurse station computer or bedsidecomputer), laptop computer, server, processor, etc. The cliniciandevices 424 may also be configured to operate one or more application(s)configured to obtain and display patient data, including a nutritionalstatus of a patient (and any related alerts/alarms) determined by thenutritional status diagnostic component 402 and/or the nutritionaltherapy component 404. The clinician devices 424 may also includeapplications that enable nutritional prescriptions to be created andtransmitted to the pharmacy preparation system 420.

The example EMR server 426 is configured to store patient medicalrecords, including a nutritional status of a patient determined by thenutritional status diagnostic component 402. In some embodiments, theEMR server 426 is configured to receive and store alarms and/or alertsgenerated by the analysis server 408 regarding a nutritional status of apatient. In other embodiments, the EMR server 426 uses a receivednutritional status of a patient in conjunction with soft tissue peakdata and/or related soft tissue peak information to determine one ormore alarms/alerts. The EMR server 426 may be configured to transmitalarms/alerts in one or more messages 430, via the network 410, to theclinician device 424 and/or the pharmacy preparation system 420.

In some embodiments, the analysis server 408 is shared logically and/orphysically between the nutritional therapy component 404 and thenutritional status diagnostic component 402. In these embodiments, theanalysis server 408 may be part of both components 402 and 404. Forinstance, the analysis server 408 may include a soft tissue engine 440configured to determine and/or evaluate a nutritional status of apatient and a nutritional analysis engine 442 configured to determineand/or recommend if a patient is to undergo a nutritional therapy and ifso, parameters of the therapy and/or components of a solution. In theseexamples, the nutritional analysis engine 442 determines (or recommends)whether a patient is to undergo a nutritional therapy based on thedetermined nutritional status. The nutritional analysis engine 442 mayalso determine and/or recommend parameters of the therapy and/orcomponents of a nutritional solution, which are transmitted in one ormore message(s) 444 to the pharmacy preparation system 420. Theparameters of the therapy may be incorporated by the pharmacypreparation system 420 into the nutritional therapy pump prescriptionmessage 425. Additionally, the pharmacy preparation system 420 mayprepare a nutritional solution based on components or therapy parametersprovided by the nutritional analysis engine 442.

FIG. 5 shows an alternative embodiment of the medical environment 400 ofFIG. 4. In FIG. 5, the analysis server 408 is configured to include onlythe soft tissue engine 440, according to an example embodiment of thepresent disclosure. In this alternative embodiment, the nutritionalanalysis engine 442 is configured, or otherwise provided within apharmacy computer 460 of the pharmacy preparation system 420. Theplacement of the nutritional analysis engine 442 means that nutritionalparameters are determined at the pharmacy preparation system 420 ratherthan the analysis server 408. Similar to the environment 400 of FIG. 4,the analysis server 408 is configured to provide the nutritional statusof the patient (and/or soft tissue peak information) in one or moremessage(s) 414. However, in the environment 400 of FIG. 5, the pharmacypreparation system 420 uses the information in the messages 414 todetermine pump parameters and/or components of a nutritional solution.

In yet alternative embodiments, the nutritional analysis engine 442 maybe included within the EMR server 426. In these instances, the analysisserver 408 is configured to provide the nutritional status of thepatient (and/or soft tissue peak information) in one or more message(s)414 to the EMR server 426. After receiving the data, the nutritionalanalysis engine 442 at the EMR server 426 determines if and/or evaluateswhether a patient is a candidate to undergo a nutritional therapy and ifso, determine and/or recommend appropriate pump parameters and/orsolution components. The EMR server 426 then provides the pump orsolution information to the pharmacy preparation system 420 to programthe pump 422 and prepare the solution. In some instances, the EMR server426 may transmit the parameters directly to the pump 422 (afterreceiving approval from the clinician device 424 or the pharmacypreparation system 420), thereby bypassing transmission of thenutritional therapy pump prescription message 425 through the pharmacypreparation system 420. Transmission of the parameters may include theEMR server 426 being configured to create the nutritional therapy pumpprescription message 425 for transmission to the pump 422.

The medical environments 400 of FIGS. 4 and 5 also include a hospitalinformation system (“HIS”) 450. The example HIS 450 is configured tomanage the aspects of a hospital's operation, such as medical,administrative, financial, and legal issues, and the processing ofservices. The HIS 450 may manage or create secure tunnels or paths tocommunicate sensitive medical data in instances in which at least someportions of the components 402 and 404 are external to a local hospitalnetwork. For example, the HIS 450 may create a virtual private networkto enable external clinician devices 424 to view patient data stored atthe EMR server 426. The HIS 450 may also facilitate the exchange ofpatient data stored in medical records with the various departments orfunctional areas of the medical environment 400.

The network 410 illustrated in FIGS. 4 and 5 may include a wide areanetwork (“WAN”) such as the Internet. The network 410 may also include alocal area network (“LAN”) and/or a wireless LAN. In some embodiments,the network 410 may include a combination of a WAN and LAN. Further, thenetwork 410 may include one or more firewalls, gateways, routers,switches, etc., for routing communications among the devices 406, 408,420, 422, 424, and 426. The network 410 may also be configured to enablethe HIS 450 to create secure connections to enable devices external to asecure medical network to receive and transmit messages with deviceswithin the secure medical network.

I. Nutritional Status Diagnostic Component Embodiment

As discussed above, the medical environments 400 of FIGS. 4 and 5include the nutritional status component 402 to determine and/orevaluate a nutritional status of a patient from one or more medicalimages 412. As described below, the nutritional status component 402,including the soft tissue engine 440, is configured to generate ameasurement or characterization of total lean body mass, referred toherein as the nutritional status of a patient. To determine a patient'snutritional status, the nutritional status component 402 may determineand/or evaluate a quality and quantity of a patient' muscle tissue. Inmany embodiments, the nutritional status component 402 determines aquality and quantity of skeletal muscle (e.g., the psoas muscle), whichprovides an indicator of total body protein or amino acid stores. Giventhe relation of amino acid stores in the body to postoperative successrates and complications (as shown in the graph 100 of FIG. 1), themeasurement of a patient's nutritional status provides an objectiveindicator of postoperative risk. The nutritional status indication maybe used by the nutritional therapy component 404 to determine and/orrecommend at least one nutritional therapy to increase amino acidavailability and improve a patient's postoperative outcome. Thefollowing section provides information regarding how a patient'snutritional status is determined and/or evaluated using at least onemedical image 412 according to the present disclosure.

The example imaging devices 406 are configured to acquire at least onemedical image 412 of a patient. The at least one image 412 is used bythe soft tissue engine 440 of the analysis server 408 to determineand/or evaluate a nutritional status of the patient. The imaging, andhence the determination of nutritional status, may occur before apatient is to undergo a medical procedure, such as chemotherapy. In someinstances, the imaging may occur when a patient is brought into ahospital or clinic after experiencing a traumatic injury or afterdeveloping a serious disease.

In some embodiments, the imaging devices 406 are CT scanners, such asthe Iqon Spectral™ or Ingenuity Flex™ CT scanners manufactured byPhillips® or the Revolution™, Optima™, or BrightSpeed™ CT scannersmanufactured by the General Electric Company®. In these instances, theimaging devices 406 are configured to take combinations of X-ray imagesfrom one or more angle(s) to produce cross-sectional (e.g., tomographic)images or virtual slices of a patient's anatomy. The images 412 arerecorded of a specific portion of a patient's anatomy, such as a thoraxregion, abdomen region, a pelvic region, etc. The cross-sectional images412 may be lateral or axial, showing patient anatomy at differentnorth-south elevations. The cross-sectional images 412 may alternativelyor additionally be longitudinal, showing patient anatomy at differenteast-west sections.

While the imaging devices 406 are referred to herein as X-ray-type CTscanners, it should be appreciated that other medical imaging devicesmay be used. For example, the imaging devices 406 may include positronemission tomography (“PET”) scanners, single-photon emission computedtomography (“SPECT”) scanners, computed axial tomography (“CAT”)scanners, and computer-aided/assisted tomography scanners. In someinstances, the imaging devices 406 may include magnetic resonanceimaging (“MRI”) scanners.

The imaging devices 406 may additionally or alternatively be configuredto perform radiodensity measurements on a patient's tissue usingcontrast (e.g., an intravenously injected radiocontrast agent) orwithout contrast. In some instances, the medical images 412 provided tothe soft tissue engine 440 may include a combination of contrast andnon-contrast images. In these examples, the imaging device 406 maydetermine and/or evaluate a patient's nutritional status using contrastand non-contrast medical images 412 of the same area. In these examples,the soft tissue peak data and soft tissue information determined fromthe contrast and non-contrast images may be averaged or otherwisecombined.

Each of the example medical images 412 shows a radiodensity level ofpatient tissue. The medical images 412 may comprise a two-dimensionalcross-sectional slice (such as the images 200 and 300 of FIGS. 2 and 3).Each image 412 may have a size between 256×256 pixels to 2040×2040pixels, for example. Preferably, each two-dimensional image may have512×512 pixels with two bytes of color data per pixel. Accordingly, eachtwo-dimensional image 412 may contain about 3.3 gigabytes (“GB”) ofradiodensity data. Each pixel of the medical images 412 may be colorcoded by radiodensity level. Further, the medical image 412 may includemetadata that specifies a radiodensity level for each pixel. Themetadata may also identify the patient, a time/date that the scan wasperformed, and an identifier of the imaging device 406 that performedthe scan.

Each of the medical images 412 may be stored in a file. For instance,each medical image 412 may be stored as a Digital Imaging andCommunications in Medicine (“DICOM”) image. In these instances, themetadata specifying the radiodensity values may be stored within aheader of the file, while the image information is stored within apayload section of the file. In other embodiments, the medical image 412may include another file type, such as a Neuroimaging InformaticsTechnology Initiative (“NIfTI”) file, a Minc file, and/or an Analyzefile.

The imaging devices 406 may be configured to record between about 50 toabout 150 images per scan of a patient. Scans of the abdominal regiongenerally yield about 75 images. However, the exact number of images isset by technicians operating the imaging devices 406. In some instances,one or only a few images may be required since the nutritional statusanalysis may be conducted on a single image of, for example, the psoasmuscle in the L3 or L4 region. Reducing the number of images recordedreduces a patient's radiation exposure.

The example soft tissue engine 440 is configured to analyze the medicalimages 412 to determine and/or evaluate muscle quality and/or quantityfor approximating a nutritional status of a patient. FIG. 6 shows adiagram of the soft tissue engine 440 of FIGS. 4 and 5, according to anexample embodiment of the present disclosure. The blocks shown in FIG. 6may be implemented as software modules, applications, algorithms, and/orroutines operating within the soft tissue engine 440. It should beappreciated that some of the blocks may be combined and/or omitted, suchas segmentation processor 622. Further, some of the blocks may beimplemented in different physical locations on the analysis server 408.For instance, the analysis server 408 may include blade servers orprocessors distributed across a computing environment such as a cloudcomputing environment. Each of the blocks illustrated in FIG. 6 mayaccordingly be hosted or implemented in different physical and/orvirtual locations within a distributed environment. Each of the blocksshown in FIG. 6 may therefore be implemented or operated by separate (orthe same) processors. Moreover, separate instances of each of the blocksmay be initiated for each set of images 412 analyzed.

a. Image Interface

To receive medical images 412 from the imaging devices 406, the examplesoft tissue engine 440 of FIG. 6 includes an image interface 602. Theexample image interface 602 may be configured to passively receive themedical images 412 from the imaging devices 406. For instance, after ascan has been completed, the imaging device 406 transmits the medicalimages 412 to the image interface 602. Alternatively, the imageinterface 602 may periodically poll the imaging device 406 to determineif new images are available. In some instances, the imaging device 406may transmit the medical images 412 to a workstation. In theseinstances, an operator reviews the medical images to confirm they arevisually clear and that a patient did not move during the scan. Afterthe images 412 are indicated as being acceptable, the workstationtransmits the images to the image interface 602.

The image interface 602 is configured in one embodiment to queue medicalimages 412 until they are to be analyzed and/or processed. The imageinterface 602 may provide status information to a user interface 604.The status may include an indication as to which medical images 412 areawaiting analysis, scheduled to be analyzed, and/or are in the processof being analyzed. For example, a technician may view a status of themedical images 412 through the user interface 604.

b. User Interface

The example user interface 604 is configured to provide administrativeaccess and control to process and analyze the medical images 412. Theuser interface 604 may be configured to render requested informationinto a format for display. Such information can include a status ofmedical images 412, soft tissue peak information, Hounsfielddistribution data, image segmentation information, and/or imageradiodensity information. The user interface 604 may also include aviewer configured to render the medical images 412 for display. The userinterface 604 may also include features that enable a technical tomanually process and analyze an image to determine muscle quality and/orquantity. However, while the user interface 604 enables a manualprocessing, it should be appreciated that the features of the softtissue engine 440 described herein are completed without userintervention.

In some embodiments, the clinician device 424 may access the userinterface 604 to view the medical images 412 and target medical images609. In addition, the clinician device 424 may access the user interface604 to view muscle quality and/or quantity data (and/or a nutritionalstatus of a patient) determined from the medical images. In theseexamples, the user interface 604 may present a list of patients forselection. Upon receiving a selection from the clinician device, theuser interface 604 may determine which images and/or data is availablewithin the soft tissue engine 440. A clinician may view the imagesand/or data on the clinician device 424. Further, the user interface 604may interact with the clinician device 424 to enable a clinician tomodify, amend, and/or make notes to the images and/or data.

c. Controller

The example soft tissue engine 440 of FIG. 6 includes a controller 606configured to provide instructions to the imaging devices 406. In someinstances, only a single medical image (or a few medical images) isneeded to determine a patient's nutritional status. Instead ofsubjecting the patient to x-rays to acquire approximately 100 images andselecting the desired image, the example controller 606 instructs theimaging devices 406 as to which specific location on a patient is neededfor imaging. Such a configuration reduces the amount of radiation towhich a patient is exposed.

In an example, the soft tissue engine 440 may receive, via the userinterface 604 for example, an indication of a specific patient that isto undergo a CT scan to determine a nutritional status of a patient. Thecontroller 606 determines a specific region on the patient where the CTscan is to be completed. The specific region may include, for example ascan of the patient's psoas muscle between the L3 and L4 vertebra. Thecontroller 606 may also receive an indication as to which imaging device406 is to image the patient. For instance, an identifier of the imagingdevice may be input into the user interface 604. Alternatively, thecontroller 606 may access the patient's medical record stored in adatabase accessible by the EMR server 426. After determining whichimaging device 406 is to image a patient, the controller 606 transmits amessage 607 to the imaging device 406 indicative of the region on thepatient to be imaged.

In some examples, the controller 606 may be omitted or not used. Forexample, in many instances, patients are given a CT scan as a standardpractice upon entering an emergency care area or before a significantmedical procedure. In these circumstances, an entire region of interestis scanned for medical diagnosis. Here, the image interface 602 receivesa copy of the medical images 412. In this manner, a separate CT scandoes not need to be completed to determine a nutritional status of apatient. Instead, medical images already acquired for other purposes maybe used to determine the patient's nutritional status.

d. Image Selector

The soft tissue engine 440 includes an image selector 608 for instancesin which more than one medical image 412 is received in the imageinterface 602. Here, the image selector 608 determines or identifies atarget medical image 609 for further analysis. Specifically, the imageselector 608 may be configured to analyze multiple medical images 412 todetermine which medical image contains sufficient patient anatomy todetermine skeletal muscle quality and/or quantity. For example, theimage selector 608 may identify a medical image that includes the psoasmuscle or the thoracic muscle.

The example image selector 608 determines a target image 609 byidentifying locations and quantities of bone tissue. For instance, theimage selector 608 may identify locations of rib and hip bone tissue todetermine a location between the L3 and L4 vertebra. In someembodiments, the image selector 608 is configured to identify withineach two-dimensional image pixels that correspond to a radiodensityabove 300 HU, which is the radiodensity associated with bone tissue.Counting pixels with a radiodensity above 300 HU provides a relativelyprecise estimation of bone tissue surface or cross-sectional area pereach two-dimensional medical image.

FIG. 7 shows a graph 700 that illustrates bone surface orcross-sectional area in square centimeters (“cm²”) for a patient'slumbar region. The surface or cross-sectional area in centimeters may bedetermined by summing the total number of pixels, where each pixel has aknown surface area in centimeters. The graph 700 shows that it islimited to medical images 36 (left end) to 48 (right end) in a set ofmedical images that ranges from, for example, 1 to 80. In the examplegraph 700, images 36 and 37 include the L3 vertebra and the lastfloating rib, which corresponds to a larger bone surface area of around14 cm². Images 42 and 43 include an area between the L3 and L4vertebras, which include less bone surface area of around 9 cm². Images44 to 46 include the L4 vertebra, which shows an increase in bonesurface area to about 11 cm². Image 48 includes a top of the pelvicwing, which is usually coplanar with the L4 vertebra. The inclusion ofthe pelvic wing causes the bone surface area to increase to above 12cm².

The example image selector 608, in the example of FIG. 7, is configuredto progress through the medical images 412 to determine the lumbarregion between the L3 and L4 vertebras. The image selector 608 thendetermines a bone surface or cross-sectional area by counting a numberof pixels in each of the medical images 36 to 48 that have aradiodensity greater than 300 HU. The image selector 608 identifies, asthe target medical image, the medical image that has a lowest bonetissue surface area in the lumbar region. In other words, the imageselector 608 determines which of the medical images represented in graph700 has a minimum tissue surface area.

The example image selector 608 is configured to identify the lumbarregion based on the relation between the medical images. For example,the image selector 608 may be configured to analyze medical imagesnumbered 25 to 45 for any CT scan of a patient's mid-section, whichgenerally correspond to the lumbar region for virtually all patients. Inother examples, the image selector 608 is configured to identify thepelvic wing tip and/or last floating rib, which are relatively easy toidentify within a set of medical images of a patient's midsection. Forinstance, the image selector 608 may search certain anatomical areas ina sequence of images 412 (corresponding to locations of the wing tips)to determine which images have radiodensity values around 300 HU inthose areas. Once the pelvic wing tip is identified, the image selector608 identifies the previous 10 to 15 medical images, or approximately7.5 cm up from the pelvic wing tip, to determine which medical imagesare to be analyzed for bone surface or cross-sectional area.

In some examples, the image selector 608 may segment or otherwiseidentify a specified portion of each medical image to identify a targetmedical image. In these examples, the image selector 608 determines acenter of mass in the patient's lumbar region. For instance, the imageselector 608 may select any medical image numbered 25 to 50, whichgenerally correspond to the lumbar region for most (if not all)patients. The image selector 608, for the selected image, determines acenter of mass by determining a center, or approximate center within thepatient's anatomy shown within the selected medical image.

For example, medical images 800 and 900 respectively of FIGS. 8 and 9show a determined center-of-mass 802 (or center-of gravity), accordinglyto example embodiments of the present disclosure. The center-of-mass 802may be determined for one medical image, such as image 800 and thenapplied to subsequent medical images, such as the medical image 900. Inother examples, the image selector 608 is configured to determine acenter-of-mass for each image. The medical images 800 and 900 are of thesame patient at two different cross-sectional slices. The medical image800 of FIG. 8 shows an end of a last floating rib 801. By comparison,medical image 900 of FIG. 9 shows the emergence of the pelvic wing 901.

The image selector 608 uses the center-of-mass 802 in FIGS. 8 and 9 as apoint of reference. The image selector 608 creates a polygonal region804 that has a top-left corner located at a defined distancehorizontally offset outwardly from the center-of-mass 802. The defineddistance may be between one cm and eight cm. In some instances, theimage selector 608 may determine the defined distance based on a heightof the patient, where a greater distance is selected for largerpatients. The purpose of the offset is to exclude the backbone andvertebra from the bone surface or cross-sectional area analysis.

The polygonal region 804 of FIGS. 8 and 9 may be sized to encompass theright-side ribs and pelvic wing of the patient. As mentioned above, themedical image 800 of FIG. 8 shows an end of a last floating rib 806,while the medical image 900 of FIG. 9 shows the emergence of the pelvicwing 902. Medical images in numerical order between medical images 800and 900 should contain no bone tissue within the polygonal region 804because there is generally no bone (other than the vertebra) locatedbetween the last floating rib 806 and the top of the pelvic wing 902 inthis region.

The image selector 608 is configured to apply the same polygonal region804 to the medical images associated with the lumbar region (e.g.,medical images of slices between the L3 and L4 vertebras). The imageselector 608 then determines tissue radiodensity for the pixels withinthe polygonal region 804. Medical images that contain substantially nobone tissue (e.g., images with substantially no radiodensities above 300HU in the polygonal region 804) correspond to images that may beselected for further analysis (e.g., target medical images). Since thepsoas muscle 808 naturally decreases in size further down the backbone(as shown in medical images 800 and 900), the image selector 608 isconfigured to select the medical image with the highest sequence numberthat does not contain bone tissue within the polygonal region 804 (e.g.,the medical image right after the last floating rib).

After identifying the target medical image 609, the image selector 608is configured to provide or transmit the image automatically for furtherprocessing. In some instances, the entire medical image may be analyzedto determine muscle quality and quantity. In other instances, only aspecified portion of the medical image is analyzed to determine musclequality and quantity. While the image selector 608 is disclosed above asselecting one target image 609, it should be appreciated that the imageselector 608 may select multiple images for analysis. For instance, theimage selector 608 may provide multiple images between the L3 and L4vertebras. Further, the image selector 608 may provide a non-contrastmedical image and a contrast medical image.

In some instances, the image selector 608 may transmit a message to theuser interface 604 that is indicative of the selected target image(s)609. The user interface 604 may display a message to a technicianincluding the identified image(s) 609. The technician may then reviewthe medical image(s) 609 to determine if the images are acceptable fordetermining muscle quality and/or quantity. After receiving anindication of approval from the user interface 604, the image selector608 transmits the medical image for further analysis. If a disapprovalindication is received (in instances where a technician may desire adifferent image or find fault with the selected image), the imageselector 608 may select another image using the methods described above,in which the disapproved image is removed from consideration.Additionally or alternatively, the image selector 608 may provide asmall subset of medical images for the technician to choose from todetermine muscle quality and/or quantity.

e. Image Analyzer

The example soft tissue engine 440 of FIG. 6 includes an image analyzer610 configured to determine a distribution of radiodensity for thetissue shown within the pixels of the target medical image 609 providedby the image selector 608. The example image analyzer 610 is alsoconfigured to determine muscle and/or tissue quantity provided withinthe target medical image 609 by analyzing the pixel data within theimage and/or the radiodensity data within the metadata. The imageanalyzer 610 is configured to generate a distribution of radiodensitydensity at different levels or bins to enable muscle quality to bedetermined.

FIG. 10 shows a diagram of a target medical image 609 a that may beanalyzed by the image analyzer 610 to determine tissue quantity. Thetarget medical image 609 a includes pixels that are color-coded based ona radiodensity value detected in the corresponding region of thepatient. The target medical image 609 a counts a total number of pixelsthat have the same radiodensity value or level. The image analyzer 610may then convert the pixel count to surface or cross-sectional area insquared centimeters based on known dimensions of each pixel. The imageanalyzer 610 then creates a distribution of the of the total tissuesurface area for each radiodensity value or level. A 512×512 pixelmedical image has approximately 262,000 pixels of data, which providessufficient resolution to adequately determine tissue quantity.

FIG. 11 shows a diagram of a distribution graph 1100 (e.g., a softtissue nutritional histogram (“nutrition-gram”) illustrating totaltissue pixel counts for each radiodensity value in HU from the targetmedical image 609 a of FIG. 10, according to an example embodiment ofthe present disclosure. The distribution graph or nutrition-gram 1100may accordingly be referred to as a Hounsfield distribution or datadistribution 612 a. It should be appreciated that in other embodiments,the surface or cross-sectional area may be represented in squaredcentimeters. The nutrition-gram 1100 may only be illustrative of adistribution created by the image analyzer 610. For example, during use,the image analyzer 610 may compute a distribution for each radiodensitylevel that comprises a series of numbers stored in a file or database.Each row may represent a different radiodensity value and include anumber of corresponding pixels and/or computed surface area.

In the example discussed in connection with FIGS. 10 and 11, the imageanalyzer 610 identifies pixels that have a radiodensity value within aspecified range, such as for example, −150 HU to 150 HU. This examplerange encompasses all muscle and fat tissue. This range, however,excludes bone tissue and some organ tissue, which is shown in FIG. 10 asdark pixels 1008. Limiting the analysis to a certain radiodensity rangereduces the amount of pixels that need to be analyzed or counted withoutaffecting the results.

In the example discussed in connection with FIGS. 10 and 11, thecolor-coding of the pixels has been simplified for readability wheredesignated region 1002 corresponds to fat tissue (i.e., visceral adiposetissue and subcutaneous adipose tissue), designated region 1004corresponds to transitional tissue (i.e., intramuscular adipose tissue),and designated region 1006 corresponds to muscle tissue. It should beappreciated, however, that in many embodiments, the shading or coloringof the pixels may be as complex as 2 bytes.

In some embodiments, the image analyzer 610 is configured to use binningto create the distribution 612 a illustrated in the nutrition-gram 1100of FIG. 11. For example, the image analyzer 610 may create radiodensitybins that have a width between 0.1 HU to 2 HU. The image analyzer 610determines which radiodensity values fall within each bin. The imageanalyzer 610 then counts the number of pixels in each bin to create thedistribution.

In some embodiments, the image analyzer 610 is configured to use one ormore filter(s) to smooth the distribution data 612 a. For example, theimage analyzer 610 may be configured to apply a Savitzky-Golay digitalfilter to smooth the data 612 a of the nutrition-gram 1100. In otherinstances the image analyzer 610 may use a moving-average filter, amultipass filter, or other type of convolution filter. The smoothing ofthe distribution data enables the data to be more easily analyzed tosearch for localized peaks, such as a soft tissue peak. Without datasmoothing, there is a high frequency of miniature peaks that makesearching for a larger local peak more difficult. The filter accordinglyremoves a noise-element from the data to enable more efficientdownstream data processing. The use of the filter also increases theprobability of identifying the soft tissue peak.

The example image analyzer 610 is configured to output certaindistribution data 612 for further processing to determine, for example,a soft tissue peak and related information. FIG. 12 shows a diagramillustrative of muscle quality and/or quantity distribution data 612that may be determined, stored, and transmitted by the image analyzer610. The distribution data 612 may be stored in one or more files tomemory 614. For example, the target medical image 609 may be stored in afirst file, while tissue pixel data 1202 and surface area data 1204 arestored in a second file. In some instances, the second file storing thetissue pixel data 1202 and surface area data 1204 may include a link orreference to the target medical image 609 stored in the first file.Distribution data 612 that may be determined by the image analyzer 610includes, for example, tissue pixel data 1202 for all tissue types inthe target medical image 609 (or all tissue within a specified range), atotal tissue surface area 1204 a, a muscle surface area 1204 b, and/or atransitional tissue surface area 1204 c. The surface area may beexpressed as a pixel count or squared centimeters. Further, the data 612may be binned and/or stored within a distribution graph, such asnutrition-gram 1100.

The example user interface 604 is configured to make the distributiondata 612 available for display. For instance, the user interface 604 maydisplay the nutrition-gram 1100 in conjunction with the medical image609 a. Further, the surface area data 1204 may be displayed in specifiedfields. Such information enables a technician to view the analyzed dataas it is being processed or after it has been processed.

FIG. 13 shows another target medical image 609 b for a differentpatient, according to an example embodiment of the present disclosure.Additionally, FIG. 14 shows a diagram of a distribution graph ornutrition-gram 1400 with data distribution 612 b, which illustratestotal tissue pixel counts for each radiodensity value in HU from thetarget medical image 609 b of FIG. 12, according to an exampleembodiment of the present disclosure. In the example described inconnection with FIGS. 13 and 14, the image analyzer 610 analyzes themedical image 609 b to create the distribution nutrition-gram 1400similar to the methods discussed above in conjunction with FIGS. 10 and11.

In comparing FIGS. 10 and 11 with FIGS. 13 and 14, before CT scans, thepatient associated with medical image 609 a was medically diagnosed asbeing frail, while the patient associated with the medical image 609 bwas medically diagnosed as being healthy. The differences in data 612between FIGS. 10 and 11 with FIGS. 13 and 14 confirm the actual physicaldifferences between the patients and show that the frail patient didindeed have less muscle and/or a greater replacement of muscle byintramuscular adipose tissue. For instance, while the amount of fattissue 1002, 1302 are about the same between the two patients, thepatient associated with medical image 609 a has significantly lessmuscle tissue 1006 and more transitional tissue 1004 (e.g.,intramuscular adipose tissue). In particular, there is significant fatinfiltration into the muscle tissue, which is shown as the transitionaltissue 1004. In contrast, the patient associated with medical image 609b of FIG. 13 has more muscle tissue 1306 and less transitional tissue1304 (e.g., muscle tissue infiltrated by fat tissue). The examples shownin FIGS. 10 and 11 and FIGS. 13 and 14 accordingly verifies that thedistribution of radiodensity values and soft tissue peaks is correlatedwith the infiltration of muscle tissue by fat tissue, which can beautomatically quantified for accurate analysis.

f. Data Analyzer

The example soft tissue engine 440 of FIG. 6 includes a data analyzer616 to determine a soft tissue peak and related information from thedistribution data 612. The example data analyzer 616 is configured toreceive the distribution data 612 from the image analyzer 610 or accessthe distribution data from the memory 414. To determine a soft tissuepeak, the example data analyzer 616 is configured to search for a localdata peak located between −50 HU and 100 HU in the data distribution612. The data analyzer 616 is configured to search only a subset of theentire distribution since the soft tissue peak will only have aradiodensity characteristic of muscle (having a radiodensity between 40HU and 80 HU) or intramuscular adipose tissue (having a radiodensitybetween −50 HU and 40 HU). This subset also ignores visceral adiposetissue and subcutaneous adipose tissue (i.e., fat tissue), which mayhave a higher peak compared to the soft tissue peak. In addition,including a fat tissue peak in the analysis may complicate the searchfor the soft tissue peak.

After locating the soft tissue peak, the data analyzer 616 stores theradiodensity value of the soft tissue peak to a nutritional statusrecord 618. The data analyzer 616 may also determine a number of pixelsor a tissue surface area that corresponds to the soft tissue peak. Insome outlier examples, the distribution data 612 may include bimodalsoft tissue peaks. In these instances, the data analyzer 616 may recordboth peaks. Additionally or alternatively, the data analyzer 616 mayaverage the bimodal distribution to determine an average peak, which isthen recorded.

In an example, the data analyzer 616 is configured to analyze the datadistribution 612 a illustrated in the nutrition-gram 1100 of FIG. 11. Inthis example, the data analyzer 616 analyzes the distribution databetween −50 HU and 100 HU. The data analyzer 616 determines that a softtissue peak exists at about 20 HU, which corresponds to a large amountof transitional or muscle tissue within a relatively tight radiodensityrange compared to other amounts of tissue that have radiodensity valuesgreater or less than the range. The data analyzer 616 also determines asurface or cross-sectional area or pixel count value associated with thesoft tissue peak. In this example, the soft tissue peak at 20 HUcorresponds to a pixel count of about 1000 pixels.

FIG. 14 shows another data distribution 612 b in a data distributionnutrition-gram 1400 that may be analyzed by the data analyzer 616. Inthis example, the data analyzer 616 determines that the soft tissue peakis located at about 55 HU. Additionally, the data analyzer 616determines that the soft tissue peak corresponds to a pixel count ofabout 600 pixels.

In addition to soft tissue peak, the data analyzer 616 may alsodetermine information related to the soft tissue peak. For example,since the soft tissue peak is usually in a Gaussian-type ofdistribution, the data analyzer 616 may determine tissue surface orcross-sectional area within a first and/or second deviation from thepeak. In instances where the peak is skewed, the data analyzer 616 maydetermine how much tissue and/or how many pixels are within a definedradiodensity distance from the peak (e.g., a soft tissue reach). Thismay include determining a percentage of tissue or pixels that are to theright of the soft tissue peak and/or a percentage of tissue or pixelsthat have a radiodensity corresponding to muscle. Such information isindicative of a width of the peak, thereby indicating how much softtissue has a radiodensity similar to the soft tissue peak. The dataanalyzer 616 may also determine radiodensity values at the first orsecond standard deviations and/or average radiodensity values betweenthe first and second deviations.

The example data analyzer 616 may also determine ratios between the softtissue peak and adjacent valleys (e.g., fat or thin tails). For example,in reference to the nutrition-gram 1100 of FIG. 11, the data analyzer616 may determine a tissue surface or cross-sectional area or a pixelcount of transition tissue at −30 HU and a tissue surface area or apixel count of the muscle tissue at 80 HU. The data analyzer 616 thencreates the ratio by comparing the tissue surface area or pixel count atthe peak to the tissue surface area or pixel count at the valley. Theratios may indicate, for example, how much tissue is clumped at the softtissue peak compared to tissue at other radiodensities. For example, thedata distribution 612 a shown in FIG. 11 has significant tissue peak tovalley ratios, indicative that most of the muscle tissue has beeninfiltrated with fat tissue. The data distribution 612 a also shows thatthe soft tissue peak is skewed to the left, with a small percentage ofpixels to the right of the peak. This skew is also indicative that mostof the muscle tissue has been infiltrated with fat tissue. Bycomparison, the data distribution 612 b shown in FIG. 14 has lower softtissue peak to valley ratios, indicative that the muscle is relativelyfree from intramuscular adipose tissue. The data distribution 612 b alsoshows tissue with more evenly spaced radiodensity values. In addition,there is less, if any, skew in the soft tissue peak. The lack of skewmeans that there is a greater percentage of soft tissue to the right ofthe peak, indicative that the muscle tissue is relatively healthy.

FIG. 15 shows a diagram of an example nutritional status record 618 thatmay be created by the data analyzer 616, according to an exampleembodiment of the present disclosure. After determining the soft tissuepeak, the data analyzer 616 stores the soft tissue peak to the record618. As indicated above, this includes the soft tissue peak radiodensitydata 1502 and tissue surface area 1504. Further, the data analyzer 616may be configured to store the standard deviation data 1506 (includingsoft tissue, skew, reach, and/or percentage of soft tissue to the rightof the peak) and the ratio data 1508. In some instances, the dataanalyzer 616 may store at least some of the information from thedistribution data 612 to the record 618, including tissue surface areadata 1204.

The data stored to the record 618 is indicative of a nutritional statusof a patient. Specifically, the soft tissue peak, standard deviationdata 1506 (including soft tissue, skew, reach, and/or percentage of softtissue to the right of the peak), and ratio data 1508 provideindications regarding the muscle quantity and quality of a patient inrelation to intramuscular adipose tissue. A clinician may use the datastored in the record 618 to determine and/or recommend if a patient isnutritionally healthy or whether a nutritional therapy is needed.Additionally or alternatively, the nutritional therapy component 404 ofFIGS. 4 and 5 may determine whether a nutritional therapy is to beadministered based on the data within the record 618.

In some instances, the data analyzer 616 is configured to determine anumerical score or textural indication (referred to as a nutritionalstatus value 1510) of a patient's nutritional status based on theradiodensity value of the soft tissue peak 1502, the tissue area of thesoft tissue peak 1504, the standard deviation data 1506 (including softtissue, skew, reach, and/or percentage of soft tissue to the right ofthe peak), and/or the ratio data 1508. For example, the data analyzer616 may compare the radiodensity value of the soft tissue peak 1502 to apredetermined threshold (e.g., 40 HU, 45 HU, 50 HU, etc.). The dataanalyzer 616 may be configured to determine and/or recommend that apatient is nutritionally unhealthy if the soft tissue peak is below thepredetermined threshold. In other examples, multiple thresholds mayexist that correspond to different malnutrition levels. For instance, asoft tissue peak between 35 HU and 45 HU may be classified as ‘slightlymalnourished’, while a soft tissue peak between 28 HU and 35 HU may beclassified as ‘moderately malnourished’, and a soft tissue peak below 28HU may be classified as ‘severely malnourished’. In other examples, anutritional score (on a scale of 1 to 100, for example) may bedetermined based on the radiodensity value of the soft tissue peak. Forinstance, a soft tissue peak radiodensity value between −50 and 80 maybe scaled to a score between 1 and 100. Then, based on the scaled score,the data analyzer 616 may be configured to determine a textualnutritional characterization. The data analyzer 616 stores the scaledscore and/or the textural characterization to the record as anutritional status value 1510.

While the nutritional status value 1510 was described as beingdetermined from the soft tissue peak 1502, it should be appreciated thatthe other soft tissue information 1504, 1506, and/or 1508 may also beused to determine and/or evaluate the nutritional status. For example,the nutritional status value 1510 may be determined based on acombination of a patient's first standard deviation 1506 (including softtissue, skew, reach, and/or percentage of soft tissue to the right ofthe peak) of the soft tissue peak, a muscle surface area, a surface areaassociated with the soft tissue peak, and radiodensity value of the softtissue peak. Each of the different data types may be assigned a weight,which when normalized, scaled, and/or combined, is compared to one ormore predetermined thresholds to determine a nutritional status. In anexample, a patient may have a soft tissue peak at 40 HU, which alone mayindicate a patient is ‘slightly malnourished’. However, the tissuesurface area within a first standard deviation may show a widedistribution where a significant amount of tissue is muscle. The dataanalyzer 616 uses the standard deviation data to determine that thepatient has instead a ‘healthy’ nutritional status.

The nutritional status value 1510 may also be determined by comparingthe soft tissue peak 1502 and/or the related soft tissue peakinformation 612, 1504, 1506, and/or 1508 to thresholds that are adjustedbased on patient demographic information. For instance, as patients age,muscle tissue tends to degenerate and be replaced by intramuscularadipose tissue. In some embodiments, the data analyzer 616 is configuredto adjust one or more thresholds based on a patient's age. In theexample above, the value of ‘35 HU’ was described as being a thresholdbetween slight and moderate malnourishment. The example data analyzer616 may adjust this threshold (as well as the other thresholds) suchthat a detection of moderate malnourishment occurs at a lowerradiodensity. The data analyzer 616 may be programed with an algorithmthat specifies that the current age of the patient is subtracted from areference value of ‘45 HU’ and divided by 10. The result of thiscomputation is then subtracted from the radiodensity threshold at 35 HU.For a 65 year old patient, the data analyzer 616 calculates anadjustment of 2 HU ((65 HU−45 HU)/10). Thus, the threshold betweenmoderate and slight malnourishment is 33 HU instead of 35 HU.

In other examples, the data analyzer 616 may adjust the threshold(s)based on other patient demographic information, such as height, race,gender, disease affliction, injury type, subsequent scheduled medicalprocedure type, etc. For instance, the data analyzer 616 may access apatient's medical record and determine that the patient is to undergochemotherapy, which is characterized as a relatively intensiveprocedure. The data analyzer 616 may accordingly adjust the threshold(s)based on medical procedure type to ensure the patient has sufficientamino acid reserves for responding to the cancer treatment. In such anembodiment, the data analyzer 616 may store to the record 618 anindication, for instance, that the patient is nutritionally acceptablefor chemotherapy (and/or a certain class of medical procedures). Inaddition, the data analyzer 616 may determine and store an indication tothe record 618 of procedures (e.g., surgery) or medical procedureclassifications for which the patient is considered malnourished.

In some embodiments, it should be appreciated that the data analyzed bythe data analyzer 616 is self-contained to muscle quality and/orquantity data determinable from the target medical image 609. In theseembodiments, the patient's data is not compared to population data ofother patients to determine a nutritional status. This may be beneficialsince population data may not be complete or representative of thepatient undergoing the nutritional analysis. In addition, not having touse population data reduces chances of uncharacteristic patients causingthe data analyzer 616 to misidentify a nutritional status of a patient.

While the example soft tissue engine 440 can operate without populationdata, in some embodiments, the data analyzer 616 may be configured touse patient population data for determining and/or evaluatingnutritional status. The use of population data may provide an indicationof how a current patient relates to other patients with similardemographics and muscle quality metrics and whether those othercomparable patients were nutritionally healthy. In an example, the dataanalyzer 616 is configured to determine, among a pool of populationdata, individuals that have similar demographic traits (e.g., height,weight, age, gender, etc.) as the patient under analysis. The dataanalyzer 616 then compares the soft tissue peak 1502 and/or the softtissue peak information 612, 1504, 1506, and/or 1508 to the soft tissuepeaks and/or related information of the identified individuals. The dataanalyzer 616 determines there is a match if, for example, theradiodensity value of the soft tissue peak 1502 between the patient andthe identified individuals is within a predetermined threshold. The dataanalyzer 616 determines a nutritional status of the matching individualsand stores this nutritional status to the record 618. In some instances,the data analyzer 616 may average the nutritional status among thematching individuals and store the averaged value to the record 618. Inthese instances, the data analyzer 616 may weight the differentnutritional statuses based on closeness of the soft tissue peaks of thematching individuals to the soft tissue peak 1502 of the patient andcloseness of their demographic traits.

While the above disclosure focuses on the processing of a single medicalimage, it should be appreciated that the data analyzer 616 may beconfigured to determine a patient's nutritional status from two or moremedical images. For instance, the data analyzer 616 (and the imageanalyzer 610) may determine soft tissue peaks 1502 and relatedinformation 1504, 1506, and/or 1508 from one or more datadistribution(s) 612 of two or more medical images. The data analyzer 616is configured to determine a soft tissue peak for each distribution. Thedata analyzer 616 then averages or otherwise combines the soft tissuepeaks. Further, the data analyzer 616 determines a nutritional status ofthe patient based on the averaged and/or combined soft tissue peaks. Thedetermined nutritional status 1510 as well as the combined or averagedsoft tissue peaks 1502 are stored to the nutritional status record 618.In some instances, the individual soft tissue peaks may also be storedto the record 618.

In the examples in which more than one medical image is processed, thedata analyzer 616 may use statistical analysis to determine if a softtissue peak and/or related information is a statistical outlier. Forinstance, three medical images in a sequence may correspond to a softtissue peak between 45 HU and 48 HU while a fourth medical in the samesequence image corresponds to a soft tissue peak at 32 HU. The dataanalyzer 616 determines that the soft tissue peak of the fourth medicalimage is a statistical outlier. In these instances, data analyzer 616may discard the soft tissue peak information related to the fourthmedical image

g. Network Interface

The example soft tissue engine 440 of FIG. 6 includes a networkinterface 620 to transmit, for example, the nutritional status record618 to the HIS 450, the EMR server 426, the pharmacy preparation system420, the at least one nutritional infusion pump 422, and/or theclinician device 424. In instances in which the soft tissue engine 440includes the nutritional analysis engine 442, the soft tissue engine 440transmits the records internally to the nutritional analysis engine 442.In these instances, the soft tissue engine 440 may still transmit thenutritional status records 618 externally to the HIS 450, the EMR server426, the pharmacy preparation system 420, the at least one nutritionalinfusion pump 422, and/or the clinician device 424.

To transmit the record 618, the example network interface 620 isconfigured to structure the information within the record 618 into oneor more message(s) 414. In some embodiments, the network interface 620may also structure the distribution data 612 and/or the related medicalimages 609 in the one or more message(s) 414. The network interface 620may individually address the message(s) 414 to, for example, the EMRserver 426 and/or the pharmacy preparation system 420. In otherexamples, the message(s) 414 may be transmitted to a gateway server,which determines a destination recipient. For instance, the cliniciandevice 424 may subscribe with a gateway to receive information relatedto a specific patient. The gateway receives and determines that record618 is associated with the subscribed patient and transmits the recordto the clinician device 424. As provided in more details below,transmission of the nutritional status record 618 enables, for example,a nutritional therapy and/or components of a nutritional solution to beautomatically determined. The transmission of the nutritional statusrecord 618 (or individual information within the record 618, such as thenutritional status value 1510) also enables a clinician to determine anapproximate lean body mass or nutritional health of a patient, whichenables the clinician to determine if the patient may undergo anintensive medical procedure. At the least, the nutritional status value1510 provides a post-procedural risk indicator. In some instances, aclinician may attempt to reduce a patient's risk and improve aprocedural outcome by scheduling a nutritional feeding before and/orafter the procedure.

h. Segmentation Processor

The example soft tissue engine 440 has been described herein asdetermining muscle quality and muscle quality from an entiretwo-dimensional medical image. In some embodiments, the soft tissueengine 440 may use a segmentation processor 622 configured to select aportion of a medical image (e.g., segment) to determine muscle qualityand/or quantity. Segmentation focuses the quantification of musclequality and quantity on a particular area (e.g., the skeletal psoasmuscle) while disregarding internal organs, abdominal muscle, visceraladipose tissue, and/or subcutaneous adipose tissue. For instance, someinternal organs have a radiodensity between 30 HU and 60 HU, whichoverlaps with muscle radiodensity between 40 HU and 80 HU. The result isthat, in some instances, the image analyzer 610 may include organ tissuein the quantification of muscle tissue if an entire image is analyzed.

There are a number of methods that may be used to segment specificmuscle tissue. The segmentation processor 622 described herein isconfigured to use one or more of these methods to isolate certaintissue. To segment muscle tissue, for example, the segmentationprocessor 622 is configured to receive one or more target medical images609 from the image selector 608. In embodiments where the segmentationprocessor 622 is used, the image selector 608 may be programmed to sendimages to the segmentation processor 622 instead of directly to theimage analyzer 610. In other embodiments, a technician may provide anindication, via the user interface 604, that one or more target medicalimages 609 are to be segmented. In these embodiments, the technician mayselect a segmentation method, if more than one is available. Further,the user interface 604 may be configured to display an image aftersegmentation and enable a technician to adjust the segmentationboundaries.

The segmentation processor 622 is configured to send the segmented imageto the image analyzer 610 after segmentation is complete. A datadistribution 612 is determined from the segmented image using the sametechniques described above in conjunction with discussion regarding theimage analyzer 610. However, the image analyzer 610, in theseembodiments, is configured to analyze the pixels within the segmentedboundary and disregard the other pixels. The segmentation processor 622may use any one of the methods described below individually or incombination.

I. Internal Organ Segmentation

In one method, the example segmentation processor 622 is configured toremove internal organs and tissue from one or more target medicalimage(s) 609. In contrast to core or skeletal muscles, abdominal cavityorgans and vasculature have relatively little symmetry with respect tothe sagittal plane in an axial CT image. The segmentation processor 622may be configured to perform statistical similarity quantification(e.g., determine an SSIM index) of the anatomic structure in atwo-dimensional medical image. The segmentation processor 622 comparesthe statistical similarity quantification of the anatomy to a threshold.Here, the segmentation processor 622 may divide a medical image into twohalves and compare the shapes of the tissue at the same location on eachof the halves. Based on correlations between the shapes in each half,the segmentation processor 622 assigns a similarity value to each of thepixels. Anatomy or pixels that are below the threshold are removed,disregarded, or segmented by the segmentation processor 622. Incontrast, anatomy or pixels that are greater than the threshold areretained within the medical image for processing by the image analyzer610.

FIG. 16 shows an example of segmentation capable of being performed bythe segmentation processor 622 on the medical image 609 a of FIG. 10.For clarity, the illustrated segmentation was performed manually.However, the illustrated segmentation is representative of tissuesegmentation that may be performed by the segmentation processor 622.The medical image 609 a of FIG. 16 shows region 1602 where the internaltissue (and bone tissue) has been removed and replaced with blackpixels. As shown in the pre-segmented medical image 609 a of FIG. 10,the internal organs and surrounding fat tissue are relativelyasymmetric. In comparison, the skeletal and abdominal muscle tissuewithin regions 1004 and 1006 and surrounding fat tissue within theregion 1002 are relatively symmetric. Segmentation accordingly causesthe designated region 1002 corresponding to fat tissue (i.e., visceraladipose tissue and subcutaneous adipose tissue), the designated region1004 corresponding to transitional tissue (i.e., intramuscular adiposetissue), and the designated region 1006 corresponding to the skeletaland abdominal muscle tissue to be retained in the medical image 609 a(as shown in FIG. 15).

FIG. 17 shows a distribution graph or nutrition-gram 1700 created by theimage analyzer 610 based on the segmented medical image 609 a of FIG.16. Compared to the nutrition-gram 1100 of FIG. 11, the nutrition-gram1700 has significantly less fat, transitional, and muscle tissue.However, the tissue remaining in the nutrition-gram 1700 corresponds toskeletal muscle and related intramuscular adipose tissue, which are moreindicative of a patient's amino acid reserves and overall nutritionalhealth. It should be noted that the soft tissue peak shown in FIG. 17has a radiodensity that is about 10 HU greater than the radiodensity ofthe soft tissue peak shown in the nutrition-gram 1100 of FIG. 11. Thisdifference indicates that quantification of the organs may skew results,which may mistakenly show that a patient is less healthy than in realitysince the radiodensity of the organs overlaps with the radiodensity ofintramuscular adipose tissue. Accordingly, segmentation performed by thesegmentation processor 622 provides more accurate results regardingmuscle quality. Further, segmentation reduces a number of pixels thathave to be analyzed by the image analyzer 610.

FIG. 18 shows an example of segmentation capable of being performed bythe segmentation processor 622 on the medical image 609 b of FIG. 13.Similar to FIG. 16, for clarity, the illustrated segmentation wasperformed manually. However, the illustrated segmentation isrepresentative of tissue segmentation that may be performed by thesegmentation processor 622. FIG. 19 shows a distribution graph ornutrition-gram 1900 created by the image analyzer 610 based on thesegmented medical image 609 b of FIG. 18. In the example described inconnection with FIGS. 18 and 19, designated region 1802 has beensegmented. Similar to the results shown in FIGS. 16 and 17, the medicalimage 609 b of FIG. 18 and the nutrition-gram 1900 of FIG. 19 show thatsegmentation reduces the amount of non-critical tissue analyzed.Compared to the medical image 609 b of FIG. 13 and the nutrition-gram1400 of FIG. 14, the soft tissue peak in the nutrition-gram 1900 isrelatively unchanged. This is a result of the patient having moreskeletal muscle, which is not segmented out.

2. Center-of-Mass Segmentation

The segmentation processor 622 may also be configured to segment one ormore medical image(s) using a center-of-mass or center-of-gravityroutine or algorithm. Here, the segmentation processor 622 is configuredto compute or determine a center-of-mass of a target medical image 609.To determine a center-of-mass, the segmentation processor 622 analyzesall of the tissue pixels within the two-dimensional medical image 609 todetermine a lateral and longitudinal center. For instance, thesegmentation processor 622 determines a width of a patient's anatomy ina medical image and divides the width in half. The longitudinal centercorresponds to a middle of the width. An intersection of the lateral andlongitudinal centers is the center-of-mass. It should be appreciatedthat other center-of-mass methods may also be used. For example, aweighted average of pixel two-dimensional coordinates in the medicalimage 609 may be analyzed to determine a center or origin.

After a center-of-mass is determined, the example segmentation processor622 is configured to determine a region-of-interest, which correspondsto a polygon having a center located at the center-of-mass. For example,the segmentation processor 622 may be configured to create arectangular-shaped region-of-interest with a specified length and width.In other examples, the region-of-interest may include a square, atriangle, an oval, a circle, a pentagon, a hexagon, etc.

The segmentation processor 622 positions, overlays, or otherwise imposesthe region-of-interest with respect to the target medical image 609 byaligning a center of the region-of-interest with the determinedcenter-of-mass. The segmentation processor 622 designates tissue withinthe region-of-interest as segmented tissue to be analyzed by the imageanalyzer 610. Tissue outside the region-of-interest is segmented outfrom analysis by the segmentation processor 622.

In some instances, the segmentation processor 622 is configured toiteratively segment the target medical image 609 to more accuratelyinclude, for example, the psoas muscle. For instance, after using themethod described above to segment, the segmentation processor 622determines a new center-of-mass within the region-of-interest using onlybone tissue. Using only the bone tissue in the center-of-mass analysiscauses the center-of-mass to shift downward toward the L3 vertebra orspine. A second region-of-interest may then be created at the newcenter-of-mass. The second region-of-interest may also have arectangular shape. However, the second region-of-interest may havesmaller dimensions to further focus on or isolate the psoas muscles.

The segmentation processor 622 may perform another iteration on thesecond region-of-interest by calculating a third center-of-mass withinthe second region-of-interest using all tissue within the region. Thisadditional iteration may move the center-of-mass to a center of thepsoas muscles. In some embodiments, the bone tissue may be disregardedin the third center-of-mass analysis. After the third center-of-mass hasbeen determined, the segmentation processor 622 creates a thirdregion-of-interest that is dimensioned to include primarily the psoasmuscles. The segmentation processor 622 then transmits the segmentedmedical image to the image analyzer 610 to determine muscle quality andquantity.

FIG. 20 shows a diagram of an example a medical image 609 c that hasbeen segmented using a center-of-mass approach. The illustrated exampleshows a region-of-interest 2002 after the third iteration. Theregion-of-interest 2002 has a center-of-mass 2004 that is centeredwithin the psoas muscles 2006. This segmentation isolates the musclequality and quantity analysis to only the psoas muscles, which areaccurate indicators of total body amino acid reserves (and overallpatient nutritional status). This segmentation method also accounts forabnormalities in a patient, where muscle shape may be asymmetric ordistorted due to injury or degradation of the muscles with age ordisease. Further, computation of a center-of-mass is relativelycomputationally efficient compared to pattern or template matching,which is described below.

3. Shape and Template Matching Segmentation

The example segmentation processor 622 of FIG. 6 may also use one ormore shape or template matching method(s) to perform segmentation. Insome embodiments, the segmentation processor 622 may combine the methodsdisclosed below with the segmentation methods discussed above to furthersegment certain muscle tissue. For example, after a region-of-interestis determined, the segmentation processor 622 may apply one or moreshape or template matching techniques to further segment muscle tissue.

A first method, described in a white paper by Chung at al. titled,“Automated Segmentation of Muscle and Adipose Tissue on CT Images forHuman Body Composition Analysis”, which is incorporated herein byreference, discloses segmentation of muscle tissue from fat and organtissue using a shape deformation model and an appearance probabilitymodel. In the appearance model, Chung at al. disclose that musclesegmentation is performed by assigning a probability of a pixel in atwo-dimensional image (or a greyscale conversion thereof) correspondingto muscle tissue. Probabilities exceeding a certain threshold are deemedto correspond to pixels representing muscle tissue. Chung at al.disclose that the pixels representing muscle tissue are then analyzedthrough a shape deformation model to approximate surface area. Inparticular, image deformations in the muscle pixel image areparametrized using a Free Form Deformation (“FFD”) model consisting of aB-spline cubic interpolation of regular lattice points. Lattice pointdeformations are coded with respect to a mean shape estimated from a setof training images. The steps for computing shape parameters of musclefrom manually segmented images included (1) performing an affinealignment and mean shape computation, (2) performing a non-rigidalignment using an FFD model, and (3) encoding incremental deformationsusing a Principal Component Analysis (“PCA”).

A second method, described in a white paper by Popuri at al. titled,“Body Composition Assessment in Axial CT Images using FEM-basedAutomatic Segmentation of Skeletal Muscle”, which is incorporated hereinby reference, builds off of Chung at al. by limiting complexsegmentation boundaries where a deformation analysis may be needed.Popuri at al. disclose the use of a template-based segmentation approachwhere a binary template defining an initial shape is deformed vianon-rigid or deformable registration to match muscle tissue in atwo-dimensional image. Popuri at al. use a finite element method(“FEM”), which uses a non-uniform mesh adapted to contour an initialshape of the template to parameterize the deformation field. For atwo-dimensional image, Popuri at al. disclose that muscle segmentationis performed by computing an optimal segmentation boundary by optimallydeforming a template such that the template substantially matches thetwo-dimensional image. Image deformations are defined using a FEM-baseddeformable registration framework that is adapted for template-basedsegmentation.

i. Population Processor

The example soft tissue engine 440 of the nutritional status diagnosticcomponent 402 of FIG. 6 may also include a population processor 630configured to analyze one or more medical images 412 stored in awarehouse or long-term storage to create correlations between populationdemographics and muscle mass or muscle quality. The example populationprocessor 630 is configured to access or otherwise obtain medical images412 stored in the EMR server 426, a medical warehouse accessibly throughthe HIS 450, the memory 614, or any other persistent storage mediumconfigured to store patient medical records. In some instances, a usermay specify, via the user interface 604, a directory or electronicaddress of the patient information to be analyzed. In addition tomedical records, the population processor 630 may also receivecorresponding patient demographic information, physiologicalinformation, disease information, treatment information, and/ortreatment cost information.

The population processor 630 identifies the medical images 412 withinthe received information and transmits the images to the image selector608 for processing. As discussed above, the image processor 608, theimage analyzer 610, the data analyzer 616, and/or the segmentationprocessor 622 are configured to determine muscle quality and/or anutritional status for each patient whom records are available. Ininstances where medical images have been recorded at different points oftreatment, the population processor 630 is configured to request thatmuscle quality and/or a nutritional status is to be determined for eachset of medical images. The process to determine muscle quality and/ornutritional status from medical images may take a few milliseconds foreach patient. The example soft tissue engine 440 accordingly maydetermine the muscle quality and/or the nutritional status (e.g., thedistribution data 612) of hundreds-of-thousands or millions of patientswithin a matter of minutes, or at least in less than an hour.

After determining the muscle quality and/or nutritional status, theexample population processor 430 is configured to correlate the musclequality and/or nutritional status to other patient information, such asdemographics, treatment plan, and/or costs. The correlation providesmeaningful data that may be used to determine a nutritional status offuture patients or conditions for recommending nutritional therapies. Insome instances, the distribution data 612 for all the patients may beanalyzed to determine thresholds for creating labels or values for anutritional status. For example, a distribution graph or nutrition-gramof soft tissue peaks may be charted in relation to patient health. Thepopulation processor 430 (or a statistician) may identify patientcharacteristics within medical records that are related to musclequality, such as, medical diagnosis, semi-subjective analyses, BMIindices, physician notes, and/or combinations thereof. Soft tissue peaksassociated with healthy patients having normal muscle mass generallycluster between 45 HU and 60 HU while soft tissue peaks associated withpatient with decreased muscle mass, such as sarcopenia, generallycluster between 30 HU and 40 HU. Further, soft tissue peaks of patientswith decreased muscle function (i.e., patients with severe sarcopenia)generally cluster between 15 HU and 25 HU. Such clusters may be analyzedby the population processor 430 to determine thresholds for determiningvalues or indications of nutritional status.

In addition, the example population processor 430 may be configured todetermine costs associated with mistreatment or delayed nutritionaltherapy. For example, the population processor 430 may identify patientswith soft tissue peaks indicative of reduced muscle mass and/or reducedmuscle function. For these patients, the population processor 430 maydetermine which medical procedures or treatments were performed. Asmentioned above, patients with reduced muscle mass have less amino acidstores to aid in recovery. The population processor 430 can quantify thecosts associated with a prolonged recovery for these patients based onhow many days of post-procedural hospital stays were needed,post-procedural medical procedures performed to address complications,and/or whether (or when) a nutritional therapy was started. Regardingwhen a nutritional therapy is traditionally started, the AmericanSociety for Parenteral and Enteral Nutrition (“ASPEN”) providesguidelines that specify a patient is not to receive a nutritionaltherapy until 7 to 14 days after a procedure. However, a patient may bemalnourished before a procedure and will therefore continue to bemalnourished after the procedure for one to two weeks before nutritionaltherapy is started if the guidelines are followed. The populationprocessor 430 can determine the medical costs incurred, based on themedical procedures and costs in a patient's medical record, to determinehow much the delayed nutritional therapy will cost the patient and thehospital. In instances where separate CT scans were performed duringthis prolonged recovery time for the patient, the population processor430 may also correlate muscle mass decrease due to medical proceduresand post-procedural treatments.

The example population processor 630 may also be configured to correlatepatient muscle quality and/or nutritional status to post-procedural longterm care or quality of life. A patient's medical record may indicate,for example, where a patient was discharged after a medical procedure.For instance, healthy patients may be discharged from a hospital totheir homes with no follow-up care. By comparison, patients withcomplications from a procedure may be discharged to their homes with aprescription for home care or physical therapy. Patients with moreserious complications may be discharged to a nursing home or anintensive care unit (“ICU”). The example population processor 630 isconfigured to determine the long term care type, duration, and costs.The population processor 630 then correlates the long term care, type,duration, and costs to the patient's muscle quality and/or nutritionalstatus. These correlations may be useful for prescribing nutritionaltherapies to patients at risk of developing serious complications (e.g.,patients with muscle masses that are similar to muscle masses ofpatients with similar demographics that received significantpost-procedural care), thereby improving their discharge outlook andimproving recovery times.

The example population processor 630 is configured to transmit one ormore message(s) 632 to the EMR server 426 and/or the HIS 450 thatincludes a muscle quality and/or nutritional status for the analyzedpatients. The messages 632 may also include thresholds for determiningnutritional status and/or correlations between patient information andmuscle quality and/or nutritional status. The messages 632 may furtherinclude correlations between muscle quality and post-proceduralcomplications and associated costs. In some instances, the analysisserver 408 and/or the EMR server 426 may use the information in themessages 432 to determine nutritional therapy treatment recommendationsand/or guidelines.

j. Example Process to Determine or Evaluate a Patient's NutritionalStatus

FIG. 21 shows a flow diagram illustrating an example procedure 2100 todetermine and/or evaluate a nutritional status of a patient from musclequality and muscle quantity data obtained from one or more medicalimages, according to an example embodiment of the present disclosure.The example procedure 2100 may be carried out by, for example, the softtissue engine 440 of the analysis server 408, as described inconjunction with FIGS. 4 to 20. Although the procedure 2100 is describedwith reference to the flow diagram illustrated in FIG. 21, it should beappreciated that many other methods of performing the functionsassociated with the procedure 2100 may be used. For example, the orderof many of the blocks may be changed, certain blocks may be combinedwith other blocks, and many of the blocks described are optional. Forexample, in instances where the soft tissue engine 440 does not includethe segmentation processor 622, the segmentation steps may be omitted.

Procedure 2100 begins when one or more medical image(s) 412 of a patientis acquired and/or received by the soft tissue engine 440 (block 2102).The medical images 412 may include, for example, CT slices of amid-section of the patient. The medical images 412 include radiodensityfor the tissue shown within the images 412. After acquiring the images,the soft tissue engine 440 is configured to select a target medicalimage 609 from among the acquired images (block 2104). As discussed inmore detail above in connection with FIG. 6, the soft tissue engine 440may select an image by identifying which image contains the least (orless) bone tissue between an area corresponding to the patient's L3 andL4 vertebras.

After at least one target image 609 is identified, the example softtissue engine 440 determines if the target image(s) are to be segmented(block 2106). If the images are to be segmented, the soft tissue engine440 uses one or more routines and/or algorithms to segment a portion ofthe target image(s) for further analysis (block 2108). As discussedabove, the soft tissue engine 440 may segment out internal organs usinga symmetry routine. The soft tissue engine 440 may also segment bonetissue by filtering pixels based on radiodensity values. Further, thesoft tissue engine 440 may use an iterative center-of-mass routineand/or one or more shape/template matching routines to isolate certainmuscle tissue (e.g., skeletal muscle tissue) for further analysis.

After segmentation, the soft tissue engine 440 analyzes the segmentedtarget medical image(s) to create a radiodensity distribution of tissuewithin the segmented region or area (block 2110). In instances wheresegmentation is not performed, the soft tissue engine 440 creates aradiodensity distribution for the entire target medical image(s). Insome embodiments, the soft tissue engine 440 may create a distributionfor only pixels within a certain predefined radiodensity range (e.g.,−150 HU to 150 HU or −100 HU to 100 HU). The soft tissue engine 440 thenanalyzes the radiodensity distribution to locate or identify a softtissue peak (block 2112). The soft tissue engine 440 may also determineinformation related to the soft tissue peak, such, as for example,standard deviations or muscle tissue area (block 2114).

The soft tissue engine 440 stores the soft tissue peak and relatedinformation to a nutritional status record 618. In addition, the softtissue engine 440 uses at least some of the information in the record618 to determine and/or evaluate a nutritional status of the patient(block 2116). The nutritional status may be a numerical indicator, atextural indicator, or more generally, a radiodensity value of the softtissue peak. The soft tissue engine 440 stores the nutritional status ofthe patient to the record 618. Further, the soft tissue engine 440transmits the record 618, or more generally, the nutritional status ofthe patient in one or more message 414 to, for example, the nutritionalanalysis engine 442, the HIS 450, the EMR server 426, the cliniciandevice 424, and/or the pharmacy preparation system 420. The exampleprocedure 2100 may then return to block 2102 for the next patient.

k. Example Results

FIG. 22 shows a diagram of a table 2200 illustrating experimentalresults using the soft tissue engine 440 described in conjunction withFIGS. 4 to 21, according to an example embodiment of the presentdisclosure. In an experiment, CT medical images from seventy-sixdifferent patients were analyzed to determine muscle quality andquantity. The patients had an average age of 64.3 years, with a standarddeviation of +/−11.4 years. Thirty-two of the patients were male andfourty-four of the patients were female. The CT images were acquired byscanning a mid-section of each of the patients.

As a control, the medical images for each patient were manually reviewedto search for a slice that represents an area between the L3 and L4vertebras. A technician then manually segmented the psoas muscle tissue.The segmented muscle tissue was analyzed to determine a soft tissuepeak. Through this manual method, it was determined that, on average,the patients have 15.6 cm² of psoas muscle surface area, with a standarddeviation of 5.7 cm². This translates into about 2927+/−997 pixels. Theaverage soft tissue peak for these patients was determined to be about42 HU+/−8 HU.

Next, the same medical images were analyzed using the soft tissue engine440. For each patient, the soft tissue engine 440 automaticallydetermined a target image for analysis using bone tissue radiodensitydata. In a first run, the images were not segmented. It was determinedfor the first run that there was an average total tissue surface area of262.3+/−69.5 cm² in each image. This corresponds to a total pixel countof 49,365+/−12,593 pixels. The average soft tissue peak determined fromthe entire images for the first run was calculated to be about39.9+/−10.1 HU.

In a second run, the targeted medical images were segmented using acenter-of-mass iterative routine. The segmented region-of-interest hadan average area of 39.7+/−9.3 cm². This corresponds to about8,397+/−1,278 pixels in the region of interest. The tissue within thesegmented region-of-interest had an average soft tissue peak of42+/−10.2 HU.

In reviewing the results, it was determined that segmentation produced aresult that is closer to the manual method of quantifying muscle tissue.However, there was slightly more variability in the segmentation runcompared to the manual counting. This variability may have resulted froma more precise quantification of muscle and transitional tissue pixelsusing an automated approach. While analyzation of the full image in thefirst run produced a lower soft tissue peak, the closeness of the peakto the manual method indicates that this method may also be acceptablein practice, with no computational processing needed for segmenting. Thedata from the first run corresponds to overall tissue composition sincethe analyzed image included more fat or muscle infiltrated with fattissue (having a lower radiodensity) compared to images that weremanually or automatically segmented specifically on the psoas muscle.The experiment accordingly illustrated that the soft tissue engine 440is capable of automatically determining muscle quality and quantity todetermine a nutritional status of a patient.

II. Medical Application Embodiments

There are a number of medical applications that can incorporate apatient's determined nutritional status to improve outcomes or reducerisk of complications. The sections below provide examples of medicalapplications that can incorporate a patient's nutritional status orquantification of muscle mass. Examples discussed below include oncologyassessment, oncology treatment, pre-procedural treatments,post-procedural treatments, and nutritional therapy administrationlocation determinations. In addition, it should be appreciated that apatient's nutritional status and/or muscle quality may be used in otherapplications including disease management.

a. Oncology Assessment Example

When a patient has the unfortunate diagnosis of having cancer, aphysician typically performs an outcome analysis to determine thepatient's prognosis five years out. Typical prognosis includesconsidering the patient's age, overall health, cancer type, and stage ofthe cancer. Each assessment includes a cancer c-statistic that assigns aprobability to the prognosis. Such information is used by the physicianand patient in evaluating treatment options. Generally, cancer prognoseshave a c-statistic that is between 60% and 75%. This mid-rangepercentage means that any given prognosis is more likely than not to becorrect, but leave significant room for deviation. This is why there arestories of patients being told they have 1 year or less to live only tohave the patients end up living a number of meaningful years.

The example analysis server 408 and/or a clinician may use a patient'snutritional status, muscle quality, and/or soft tissue peak informationto determine a more accurate prognosis. For instance, patients withhealthier muscle tend to respond better to cancer treatments. Incomparison, patients with muscle infiltrated with fat tend to respondless well to cancer treatments. The analysis server 408 and/or aclinician may use the patient's nutritional status to improve the valueof the c-statistic. In other words, knowing the muscle quality of apatient improves the probability of the prognosis being correct. In someexamples, knowing a patient's nutritional status generated c-statisticsaround 90%. The muscle quality and/or nutritional status may accordinglybe used to improve the reliability of patient cancer assessments.

b. Oncology Treatment Example

Typical oncology treatments, such as chemotherapy, infuse medicationsinto a patient. The medications are oftentimes water-soluble orfat-soluble. Water-soluble medications are absorbed by muscle tissue. Avolume of medication distribution is determined based on a patient'sestimated lean body mass or body surface area, which take into account apatient's height and weight. Known calculations of medicationdistribution assume that every patient has a constant distributionbetween fat tissue and muscle tissue based on their height and weight.However, as discussed above, patients do not have the same muscle-to-fatratios. Some patients, especially older patients, have muscledegeneration.

The differences between muscle-fat ratios for patients mean thatpatients will absorb different amounts of chemotherapy medicationdifferently. For example, two patients may have identical, or verysimilar, heights and weights. However, one of the patients may havehealthy muscle while the other patient has significant muscledegradation. Since the patients have the same height and weight, knowncalculations would recommend the same chemotherapy dosage for eachpatient. However, the unhealthy patient has less muscle mass to absorbthe medication. This means that the muscle that is there absorbs more ofthe medication than intended. The result is that muscle of the unhealthypatient has higher concentrations of the medication. If theconcentrations exceed certain levels, it is considered an overdose thatresults in a risk of the patient experiencing affects from toxicity. Bycomparison, the healthy patient has more muscle to absorb the samedosage of medication, which means lower concentrations of the medicationper square centimeter of muscle.

The example analysis server 408 and/or a clinician may use a patient'snutritional status, muscle quality, and/or soft tissue peak informationto determine that a lower (or higher) dosage of chemotherapy medicationmay be more beneficial. For instance, the analysis server 408 maydetermine that patients with less muscle mass are to be prescribed 10%to 20% less chemotherapy medication than otherwise recommended to avoidtoxicity. In other examples, the analysis server 408 may provide analert and/or alarm to a clinician indicating that the chemotherapydosage should be revisited in view of a patient's muscle mass and/ornutritional status. The muscle quality and/or nutritional status mayaccordingly be used to improve chemotherapy treatment.

c. Pre-Procedural and Post-Procedural Examples

Oftentimes before undergoing an intensive medical procedure, such asabdominal surgery or aortic heart valve replacement, or beginningtreatment for a disease, a clinician prescribes or recommends actionsthat a patient may take to improve the outcome. This includesexercising, eating healthy, and refraining from smoking and drinking.The example analysis server 408 and/or a clinician may use a patient'snutritional status, muscle quality, and/or soft tissue peak informationto determine if a patient is to be prescribed a nutritional therapybefore, during, or after a procedure or disease treatment to furtherimprove a patient's outcome.

Currently, ASPEN recommends that a nutritional therapy is not to beadministered until at least 7 to 14 days after a patient cannot feedthemselves or after a medical procedure. During this time, the patient'smetabolism increases to help the patient recover from the procedure ortreatment. In addition, any inflammation from the procedure or treatmentusually consumes muscle tissue and leads to fat infiltration. This meansthat today malnourished patients or patients that become malnourishedare not given nutritional therapy until at least a week after aprocedure. This delay enables the malnutrition to become worse, therebyslowing the patient's metabolism and ability to recover.

The example analysis server 408 and/or a clinician may use a patient'snutritional status, muscle quality, and/or soft tissue peak informationto determine whether (and how much) nutritional therapy is needed basedon a patient's degree of malnourishment. For example, during an initialassessment, in addition to recommending that a patient exercise, theanalysis server 408 and/or a clinician may determine from a patient'snutritional status that the patient is to undergo some level ofnutritional therapy. This could include a nutritional supplementconsumed orally and/or provided subcutaneously, enterally, and/orparenterally. The goal is to establish a patient's nutritional status toenable proper nutritional treatments to be proactively prescribed toreduce changes of developing complications later.

To determine a nutritional therapy, the analysis server 408 may comparethe patient's demographics, disease state, and nutritional status topopulation data. The analysis server 408 may determine potentialoutcomes based on medical histories of similarly situated patients. Ifthe potential outcomes result in complications or low levels of recoveryassociated with malnourishment, the analysis server 408 may determinethat the patient is to receive a nutritional therapy. The parameters ofthe therapy may be recommended based on a soft tissue peak inconjunction with the procedure, disease state, and patient demographics.

In addition, the analysis server 408 may also determine or recommendpost-procedural care based on the nutritional status and/or muscle massof the patient. For example, the analysis server 408 may recommend ICUcare or nursing home care for patients with severe malnourishment. Incontrast, the analysis server 408 may recommend at-home care forpatients with moderate malnourishment. Such recommendations may bedetermined before the procedure, such that the clinician and patient areaware of most likely post-procedural care options and post-proceduralquality of life. The nutritional information may also enable theclinician and/or patient to prearrange and take appropriate measures tosetup this care. Accordingly, knowing a patient's nutritional statusenables clinicians to be more proactive to help patients avoid (orreduce the effects from) post-procedural complications.

d. Nutritional Therapy Administration Location Determination Examples

In many instances, a patient may be prescribed a nutritional treatmentthat is not administered parenterally. For example, nutrition may beprovided subcutaneously without an IV or catheter. Additionally,nutrition may be administered orally through a supplement. The exampleanalysis server 408 may be configured to provide an administrationlocation recommendation and/or determination based on a patient'snutritional status, muscle mass, disease state, and/or demographicinformation. For example, the analysis server 408 may determine thatmoderately malnourished patients may be prescribed a subcutaneoustreatment where nutrition is provided underneath a patient's skin. Whilesubcutaneous treatment cannot match parenteral or enteral in terms ofthe volume of nutritional solution that can be administered, it issignificantly less invasive and may be administered by a less skilledprofession in a patient's home or nursing home.

In other examples, the analysis server 408 may analyze population datato determine anticipated discharge conditions of patients similarlysituated to the patient under analysis. The analysis server 408determines, for example, that patients with the same demographics anddisease state typically require a stay in the ICU for at least threedays before regaining the ability to feed themselves. The analysisserver 408 may recommend a nutritional therapy, such as parenteral orenteral, which can more easily be administered in the ICU. Accordingly,knowing a patient's nutritional status enables clinicians to be moreproactive in determining how a nutritional therapy is to beadministered.

III. Nutritional Therapy Component Embodiment

In some embodiments, alarms, alerts, and/or a recommendation may begenerated based on a patient's nutritional status determined by thenutritional status diagnostic component 402. In addition, nutritionaltherapy parameters and/or components of a nutritional solution may bedetermined and/or recommended based on a patient's nutritional status inconjunction with other information, such as patient demographicinformation, physiological information, disease state, etc. The examplenutritional therapy component 404 of the medical environments 400 ofFIGS. 4 and 5 is configured to automatically manage the administrationof a nutritional therapy to a patient based, at least in part, on apatient's determined nutritional status. The nutritional therapycomponent 404 includes the nutritional analysis engine 442, which may beconfigured to use the patient's nutritional status, soft tissue peak,and/or related soft tissue peak information in the nutritional statusrecord 618 (and/or the distribution data 612) to determine a nutritionaltherapy for a patient. The nutritional analysis engine 442 may belocated in and/or operate in conjunction with the analysis server 408,the EMR server 426, and/or the pharmacy preparation system 420.

Referring again to FIGS. 4 and 5, the nutritional therapy component 404in the illustrated embodiments of FIGS. 4 and 5 includes one or moreinfusion pumps 422. The example infusion pump 422 may include any pumpcapable of delivering an intravenous and/or nutritional (e.g., a totalparenteral nutrition (“TPN”)) therapy to a patient via one or more linesets. Examples include a syringe pump, a linear peristaltic pump, alarge volume pump (“LVP”), an ambulatory pump, multi-channel pump, etc.A syringe pump uses a motor connected to a drive arm to actuate aplunger within a syringe. A linear peristaltic pump uses a rotor tocompress part of a tube while rotating. Oftentimes, one or more rollersof the rotor contact the tube for half a rotation. The compressedrotation causes a defined amount of fluid to pass through the tube. LVPstypically use one or more fingers or arms to compress a portion ofintravenous therapy (“IV”) tube. The timing of the finger actuation onthe tube causes constant or near constant movement of a fluid throughthe tube.

The example infusion pump 422 may include, for example, the Baxter®SIGMA Spectrum™ pump, which is shown in FIGS. 4 and 5. The infusion pump422 includes a display 451 and interfaces 452 that enable a clinician tospecify or program an infusion or nutritional therapy. The display 451may present a graphical code (e.g., a quick response (“QR”) code, whichmay be scanned by a clinician to associate the pump 422 with anutritional therapy pump prescription message 425 at the EMR server 426,the pharmacy preparation system 420, and/or the analysis server 408. Theinterfaces 452 may be configured to enable a clinician to programparameters from a nutritional therapy pump prescription message 425 intothe pump 422. Other examples of infusion pumps include a linear volumeparenteral pump described in U.S. Publication No. 2013/0336814, asyringe pump described in U.S. Publication No. 2015/0157791, anambulatory infusion pump described in U.S. Pat. No. 7,059,840, aninfusion pump described in U.S. Pat. No. 5,395,320, and an infusion pumpdescribed in U.S. Pat. No. 5,764,034, the entirety of each areincorporated herein by reference. The infusion pump 422 may also includethe Baxter® Colleague™ volumetric infusion pump.

The example pharmacy preparation system 420 includes any system that isconfigured to manage and prepare compound solutions (e.g., TPN solutionsand other multi-ingredient solutions) for administration to a patient.For example, the pharmacy preparation system 420 may include the Baxter®EXACTAMIX™ Compounder, which is an automated pumping system thatcompounds multiple sterile ingredients into a finished solution in oneor more patient bags. The pharmacy preparation system 420 may produce,for example, a three liter patient-ready TPN bag in approximately fourminutes once an individual patient formula has been determined.Preparation includes, for example, creating a nutritional solution byselecting and mixing together certain quantities of water, amino acids,lipids, glucose, dissolved salt, triglycerides, trace elements,vitamins, and/or nutritional supplements. In some instances, thepharmacy preparation system 420 may also select a premixed solution (ormodify a premixed solution) among a plurality of available premixsolutions such at the Clinimix™ and Clinimix E™ manufactured by Baxter®.

The example pharmacy preparation system 420 may also include a pharmacyworkflow manager 460 that is configured to automate the process ofrouting, preparing, inspecting, tracking, and reporting on thepreparation of nutritional solutions prepared by the compounder. In someembodiments, the pharmacy workflow manager 460 may include the DoseEdge™Pharmacy Workflow Manager, provided by Baxter®. The nutritional analysisengine 442, or components of the nutritional analysis engine 442, may beincluded within the pharmacy workflow manager 460. For instance, afterdetermining (or receiving an indication) that a patient is to receive aTPN therapy, the nutritional analysis engine 442 at manager 460 maydetermine administration parameters to program the infusion pump 422and/or components, compositions, and/or concentrations for a TPNsolution. The administration parameters may be provided to the pump 422in a nutritional therapy pump prescription message 425.

The example nutritional analysis engine 442 is configured to analyze anutritional status of a patient to determine if one or more alarm(s) oralert(s) is to be generated. The alarms or alerts may be sent to theclinician device 424 to place a clinician on notice about the patient'snutritional status. A clinician may accordingly use the clinician device424 to prescribe a nutritional therapy. The alarms or alerts may also besent to the EMR server 426, which may prevent, or at least generate awarning in regard to, a subsequent medical procedure. The nutritionalanalysis engine 442 may further use information associated with thenutritional status and/or patient demographic information to determine(or recommend) a nutritional therapy. This may include, for example,determining (or recommending) administration parameters, such as avolume to be infused, an infusion rate, and/or an infusion duration.This may also include determining (or recommending) components of anutritional solution.

FIG. 23 shows a diagram of the nutritional analysis engine 442 of FIGS.4 and 5, according to an example embodiment of the present disclosure.The blocks shown in FIG. 23 may be implemented as software modules,applications, algorithms, and/or routines operating within thenutritional analysis engine 442. It should be appreciated that some ofthe blocks may be combined and/or omitted. Further, some of the blocksmay be implemented in different physical locations on the analysisserver 408. For instance, the analysis server 408 may include bladeservers or processors distributed across a computing environment such asa cloud computing environment. The nutritional analysis engine 442 mayalso be distributed across one or more devices in the nutritionaltherapy component 404, including the analysis server 408, the EMR server426, and/or the pharmacy preparation system 420. Accordingly, each ofthe blocks shown in FIG. 23 may be implemented or operated by separate(or the same) processors. Moreover, separate instances of each of theblocks may be initiated for each record 618 and/or each patient.

a. Network Interface

The example nutritional analysis engine 442 of FIG. 23 includes anetwork interface 2302 to receive, for example, messages 414 includingthe nutritional status records 618. In some instances, the networkinterface 2302 may be addressable to receive the messages 414. In otherexamples, the network interface 2302 may subscribe at a gateway (locatedat or configured to operate in conjunction with the EMR server 426and/or the HIS 450) to receive messages 414 that include the nutritionalstatus records 618. In various examples, the messages 414 may include acertain identifier in a header that provides an indication of the typeof the message. To receive the messages 414, the network interface 2302in one embodiment requests for the gateway to send the messages 414 withthe certain identifier. The network interface 2302 may also subscribebased on a patient identifier. In alternative embodiments, the networkinterface 2302 may poll, for example, the soft tissue engine 440 and/orthe EMR server 426 requesting newly created nutritional status records618.

In some examples, such as when the nutritional analysis engine 442 ispart of the pharmacy preparation system 420, the network interface 2303may receive prescription information from the clinician device 424and/or the EMR server 426. For instance, a clinician may determine thata patient is malnourished through information in the record 618. Inresponse, the clinician may create a nutritional prescription at theclinician device 424, which is sent to the network interface 2302 and/orthe EMR server 426. The nutritional analysis engine 442 uses theinformation within the prescription in conjunction with the pharmacypreparation system 420 to create a nutritional solution and/or createadministration parameters to auto-program the infusion pump 422 via thenutritional therapy pump prescription message 425.

The example network interface 2302 is also configured to transmitinformation to other devices within the nutritional therapy component404. For instance, the network interface 2302 may transmit, in one ormore message(s) 444 (such as HL7 compliant messages), parameters of anutritional therapy and/or components of a nutritional solution. Thetransmission may include, for instance, a nutritional therapy pumpprescription message 425. The messages 444 and/or 425 may be transmittedto the pharmacy preparation system 420, which cause the system 420 toprepare one or more nutritional solutions. The messages 444 and/or 425may also be transmitted to the infusion pump 422 for auto-programming.The messages 444 and/or 425 may also be transmitted to the cliniciandevice 424 and/or the EMR server 426 for documentation and review.

The network interface 2302 may also transmit alerts and/or alarms in oneor more messages 430. For instance, after it is determined that an alertand/or alarm is to be transmitted, the network interface 2302 mayidentify recipients for the messages 430. Recipients may includeclinician devices 424 that have subscribed to receive alarms and/oralerts regarding the nutritional status of patients and/or cliniciandevices 424 that have subscribed to receive alarms and/or alerts relatedto certain patients. The network interface 2302 may also transmit thealarms/alerts to the EMR server 426 for documentation. In someinstances, the transmission of the alarms/alerts to the cliniciandevices 424 may be provided by the EMR server 426 after receiving thealarms/alerts from the network interface 2302. In some instances,sending the messages 430 to the EMR server 426 may cause a notificationto be generated by the EMR server 426 that a subsequent medicalprocedure should be delayed until the patient receives a nutritionaltherapy. The messages 430 may also cause the EMR server 426 to prevent,as much as possible, a subsequent medical procedure from being performeduntil the patient's malnutrition is treated.

The network interface 2302 may also transmit the messages 430 to thepharmacy preparation system 420 in instances where the nutritionalanalysis engine 442 is located at the EMR server 426 and/or the analysisserver 408. The messages 430 may provide an indication to a pharmacytechnician that a nutritional solution is to be prepared. The messages430 may also prompt a pharmacist or a clinician to determine anutritional therapy based on the information within the nutritionalstatus record 618, the patient's demographic traits, a subsequentmedical procedure, and/or a patient's disease/injury/condition.

b. Alarm-Alert Generator

The example nutritional analysis engine 442 of FIG. 23 includes analarm-alert generator 2304 configured to determine if an alarm and/oralert is to be created based on information within the nutritionalstatus record 618 and/or the distribution data 612. The examplealarm-alert generator 2304 is configured to analyze information within,for example, the nutritional status record 618 using one or moreroutines and/or algorithms to determine if an alarm and/or an alert isto be generated. In some embodiments, the alarm-alert generator 2304 mayalso use patient demographic data, population data, medical historydata, and/or physiological data in conjunction with the muscle qualityand/or quantity data in the record 618 to determine if an alert and/oralarm are to be generated. Moreover, the alarm-alert generator 2304 mayalso consider the patient's disease state, care area, and/or subsequentscheduled medical procedures to determine whether an alarm and/or analert are to be generated.

To determine if an alarm and/or alert is to be generated, thealarm-alert generator 2304 may be configured to access certain data fromexternal sources, such as the EMR server 426. For instance, thealarm-alert generator 2304 may access the EMR server 426 to access apatient's EMR, which may include medical history, demographic data,physiological data, and/or medical procedure schedule. To access aspecific record, the alarm-alert generator 2304 may transmit in arequest message a patient identifier, which was included within themessages 414, to the EMR server 426. In response, the alarm-alertgenerator 2304 receives the requested patient EMR (or specific requestedportions of the EMR). In other embodiments, the information fordetermining if an alarm and/or alert is to be generated may be includedwithin the received messages 414.

An alert is an indication that a clinician should review a patient'snutritional condition based on the determined nutritional status. Inother words, an alert provides an advisory notice that a nutritionaltherapy may be warranted for the patient. In many cases, an alert may beoverridden by a clinician such that a nutritional therapy is not needed.However, in some cases, the clinician may decide to proceed with anutritional therapy. In some embodiments, the nutritional analysisengine 442 may wait for confirmation from a clinician beforeautomatically determining solution components and/or pump administrationparameters.

In comparison to an alert, an alarm is indicative that a clinician isrequired to review a patient's medical condition based on the determinednutritional status. In many cases, generation of an alarm requires apatient to undergo a nutritional therapy, if the situation warrants,unless specific overriding instructions are received from a clinician.After generating an alarm, the nutritional analysis engine 442 may begindetermining a solution composition and/or administration parameters. Inother instances, the nutritional analysis engine 442 may be configuredto wait for a prescription or order from a clinician. In these otherinstances, the nutritional analysis engine 442 may periodically transmitthe alarm or elevate the status of the alarm until a response isreceived. Moreover, generation of the alarm may prevent a patient fromundergoing a subsequent medical procedure until the alarm is addressed.

To generate an alarm and/or an alert the alarm-alert generator 2304 isconfigured to generate one or more message(s) 430, which is indicativeof the alarm and/or the alert. The message 430 may be HL7 compliant andinclude, for example, a creation time/date, a patient identifier, and acode or text indicative of why the alarm and/or alert was generated. Themessages 430 may also include options for responding, including a linkselectable by a clinician using the device 424 to create a nutritionalprescription. The messages 430 may further include at least some of theinformation from the record 618 and/or the distribution data 612. Forinstance, the messages 430 may include a nutritional status value, asoft tissue peak, muscle surface tissue area, and/or a link to a targetmedical image 609 (or the image itself).

FIG. 24 shows a diagram illustrative of an example algorithm 2400executable by the alarm-alert generator 2304 to determine whether analarm and/or an alert are to be generated based on muscle quality dataand/or muscle quantity data, according to an example embodiment of thepresent disclosure. The algorithm 2400 is only exemplary of routinesand/or algorithms that may be used by the alarm-alert generator 2304. Inother examples, the algorithm 2400 may include additional factors, suchas physiological data, population data, care area, and patient historydata. In yet other examples, the algorithm may include fewer factors,such as using only a determined soft tissue peak and/or nutritionalstatus of a patient.

The algorithm 2400 illustrated in may FIG. 24 include three differentconditions 2402, 2404, and 2406 for generating an alarm and/or alert.Condition 2402 specifies that an alert is generated when (i) a softtissue peak radiodensity value is between 25 and 40 HU and/or anutritional status indicates ‘moderate’ malnourishment, and (ii) asubsequent procedure has a classification that is no higher than class2. In some embodiments, medical procedures may be assigned a class basedon patient risk, substantiality, and/or invasiveness. More intensiveprocedures correspond to a lower number. A class 2 procedure may includemoderate surgery, such as an ACL repair or joint repair. In comparison,a class 1 procedure may include chemotherapy, extensive surgery (such asabdominal surgery), or significant trauma-related surgery. In instanceswhere no subsequent medical procedure is scheduled, the algorithm 2400may be based on a patient's current disease state or condition.

In some examples, the algorithm 2400 may also be configured to considerpatient demographics. For example, the soft tissue peak range may beadjusted based on a patient's age and/or gender. The adjustment mayaccount for natural muscle degradation of normal individuals withsimilar to reduce the generation of false alerts. For instance, thealgorithm 2400 may shift the range downward by 1 HU for every five yearsthe patient is over 40 or 45 years old. Further, the range may beadjusted based on whether the patient is male or female, where slightlylower ranges may be used for females. In instances where muscle tissuesurface area is considered when generating an alert, the algorithm 2400may be configured to adjust thresholds based on a patient's height.Typically, taller patients have more muscle tissue. To account for this,the algorithm 2400 may normalize muscle tissue area based on height.

Conditions 2404 and 2406 specify when an alarm is to be generated. Forcondition 2404, the algorithm 2400 may determine that an alarm is to begenerated when (i) a soft tissue peak radiodensity value is between 0and 25 HU and/or a nutritional status indicates ‘severe’ malnourishment,and (ii) a subsequent procedure has a classification that is no higherthan class 2. For condition 2406, the algorithm determines that an alarmis to be generated when (i) a soft tissue peak radiodensity value isbetween 25 and 40 HU and/or a nutritional status indicates ‘moderate’malnourishment, and (ii) a subsequent procedure has a classificationthat is no higher than class 1. The difference between conditions 2404and 2406 lies with the classification of the subsequent medicalprocedure. In condition 2406, since the subsequent medical procedure isa more intensive class 1 procedure, the threshold for generating analarm is much lower (e.g., the soft tissue peak range only has to bebetween 25 HU and 40 HU). In other words, the algorithm 2400 provides amore critical response with an alarm to ensure sure a patient hassufficient amino acid reserves prior to undergoing a more substantivemedical procedure (or is afflicted with a more severe condition ordisease).

After determining that an alarm or alert is to be generated, the examplealarm-alert generator 2304 of FIG. 23 is configured to generate one ormore message(s) 430 indicative of the alarm and/or alert. The message(s)430 are transmitted to the network interface 2302 for transmission tothe appropriate devices 420, 424, and 426. Additionally, the alarm-alertgenerator 2304 is configured to begin the process of determining asolution composition and/or administration parameters, which aredescribed in more detail below.

c. Solution Processor

The example nutritional analysis engine 442 of FIG. 23 includes asolution processor 2306 configured to determine a composition,components, and/or concentration of a nutritional solution based, forexample, on a nutritional status of a patient. The example solutionprocessor 2306 is configured to determine a solution composition afterreceiving an indication from the alarm-alert generator 2304.Additionally or alternatively, the solution processor 2306 may beconfigured to receive a message from the EMR server 426 and/or theclinician device 424 providing an indication that a nutritional solutionis to be prepared. The indication may include an approval for thesolution processor 2306 to determine or recommend a nutritionalsolution. The indication may also include a nutritional therapyprescription or order that may specify, for example, a total amount ofnutrition to be administered, a desired level of amino acids, lipids,and/or glucose to be administered, and/or a type or name of anutritional solution to be administered.

In instances where the prescription or order specifies certainproperties or parameters, the solution processor 2306 is configured tocheck the values of the properties or parameters against a drug libraryor nutrition library. The solution processor 2306 may generate an alertand/or an alarm if any of the values exceed library limits. Forinstance, the solution processor 2306 may receive an order thatspecifies 200 grams (“g”) of amino acids are to be provided for everyliter (“l”) of solution. However, the nutrition library may include alimit of 125 g/l. In response to determining that value of the aminoacid parameter exceeds a limit, the solution processor 2306 transmits analert and/or an alarm to the clinician device 424. In response, theclinician may revise the order or override the limit.

In some examples, the solution processor 2306 may receive parameters ora prescription order that provides more general parameters, such as atotal volume of solution to be infused. In these examples, the solutionprocessor 2306 may use one or more of the routines or algorithmsdiscussed below to determine a composition of amino acids, lipids,and/or glucose based on the muscle quality and/or quantity data withinthe nutritional status record 618 and/or the distribution data 612. Inaddition, the solution processor 2306 may use one or more demographictraits of the patient, such as height, weight, and/or gender to refinethe solution composition determination. Further, the solution processor2306 may use the disease state/condition, care area, population data,and/or physiological parameters to refine the solution compositiondetermination.

The example below discloses one example routine and/or algorithm thatthe solution processor 2306 may execute to determine a solutioncomposition for a patient. Initially, the solution processor 2306 isconfigured to determine an ideal body weight (“IBW”) in kilograms (“kg”)for a patient using the patient's height in centimeters. For instance,the solution processor 2306 may use equation (1) below for a malepatient and equation (2) below for a female patient to determine an IBW.

IBW_(male)=48+(height−152)*1.06  (1)

IBW_(female)=45.4+(height−152)*0.89  (2)

The example solution processor 2306 is configured to determine a basedosing regimen or total volume of solution to be infused per day basedon the IBW. For instance, a routine may correlate or equate patientheight and/or IBW to a base level of solution to be administered. In anexample, an IBW of 85 kg corresponds to a base solution of 2400 ml, anIBW of 75 kg corresponds to a base solution of 2100 ml, an IBW of 65 kgcorresponds to a base solution of 1800 ml, an IBW of 55 kg correspondsto a base solution of 1500 ml, and an IBW of 45 kg corresponds to a basesolution of 1200 ml.

After determining the base amount of solution, the example solutionprocessor 2306 is configured to adjust the base solution according tomuscle quality and/or quantity data. In some examples, the radiodensityof the soft tissue peak may be used. In other examples, the surface areaof the muscle tissue and/or a standard deviation of the soft tissue peakmay be used. Equation (3) below shows can example adjustment that may beapplied to the base solution volume. In the example below, constantvalue ‘45’ is subtracted from the radiodensity value of the soft tissuepeak (“STP”). The difference is then divided by a normalization constant(f), which may include any value between 30 and 100 based, for instance,on determined correlations between soft tissue peak and treatments formalnutrition. This result, referred to as the adjustment, is thenmultiplied by the base solution volume and added to the base solutionamount to determine an adjusted solution amount. In other words, theadjustment corresponds to a percentage increase in the base solutionvolume.

$\begin{matrix}{{adjustment} = \frac{45 - {S\; T\; P}}{f}} & (3)\end{matrix}$

In an example, a patient with an IBW of 75 kg has a soft tissue peakwith a radiodensity value of 37 HU, which corresponds to moderatemalnutrition. In this example, f has a value of ‘60’. The base solutionvolume for the patient is 2100 ml based on the 75 kg IBW. The solutionprocessor 2306 uses equation (3) to determine that the base amount hasto be increased by 13.3%. The solution processor 2306 accordinglydetermines that the patient is to receive 2380 ml of nutritionalsolution to treat the patient's moderate malnourishment.

After determining a total solution to be administered per day, theexample solution processor 2306 determines an amount of amino acids,lipids, and/or glucose to be included within the solution. It should beappreciated that the solution processor 2306 may be configured to createnutritional solutions that are relatively rich in amino acids to helprestore a patient's amino acid reserves. In the above-example, theamount of amino acids to be provided in the nutritional solution rangesfrom 50 g/l to 83 g/l. Additionally, the amount of glucose ranges from67 g/l to 112 g/l and the amount of lipids ranges from 17 g/l to 30 g/l.To determine the amounts of each component, the solution processor 2306may make an adjustment similar to the adjustment described in connectionwith equation (3). For example, equation (4) may be used by the solutionprocessor 2306.

$\begin{matrix}{{adjustment} = \frac{45 - {S\; T\; P}}{c}} & (4)\end{matrix}$

Similar to equation (3), equation (4) subtracts the soft tissueradiodensity from 45 HU. The difference is then divided betweennormalization constant (c), which may be any value between 10 and 100.In some instances, the normalization constant c may be one-half thevalue off. For instance, in the example discussed above in connectionwith equation (3), the constant f has a value of 60. Accordingly, theconstant c is 30. For the same patient, the adjustment for amino acidcontent is determined to be 26.6%, which is applied to the base aminoacid amount of 50 g/l. The solution processor 2306 accordinglydetermines that the patient is to receive 63.3 g/l of amino acids in thesolution. Since the patient is to receive 2380 ml of solution per day,this means that the solution is to comprise a total of 150 g (2.380l*63.3 g/l) of amino acid per day.

It should be appreciated that the solution processor 2306 not onlyincreases the amount of solution administered to a patient, but also theconcentration of amino acids in the solution as a patient is moreseverely malnourished. Such a configuration may reduce a total number ofdays of nutritional therapy needed since the patient is receiving arelatively high level of amino acids. In alternative embodiments, thesolution processor 2306 may maintain the total solution volume butincrease the concentration of amino acids. These alternative embodimentsmay be used for patients that cannot handle larger volumes of solutionbut still need an increased dose of amino acids.

The example solution processor 2306 may perform similar calculations forthe glucose and lipid components. It should be appreciated that theadjustment is applied to the base amount for each (67 g/l for glucoseand 17 g/l for lipids). Further, different normalization constants maybe used for lipids and glucose since these components may not be ascritical to the patient as amino acids. In other examples, the glucoseand lipid amounts may be specified as ratios to the concentration ofamino acid such that the determined amount of amino acid may be used todetermine the amount of lipids and glucose. In yet other examples, theroutine or algorithm used by the solution processor 2306 may specify atable that correlates amino acid concentration to lipid and glucoseconcentrations.

Once the amount of amino acid, lipids, and glucose are determined, theexample solution processor 2306 may determine specific componentcompositions to generate the determined quantities of amino acids,lipids, and glucose. For instance, the routine or algorithm may specifythat to generate 50 g/l of amino acids, a solution should be preparedcontaining 5 g of isoleucine, 7.4 g of leucine, 9.31 g of lysine acetate(corresponding to 6.6 g of lysine), 4.3 g of methionine, 5.1 g ofphenylalanine, 4.4 g of threonine, 2 g of tryptophan, 6.2 g of valine,12 g of arginine, 3 g of histidine, 14 g of alanine, 11 g of glycine,11.2 g of proline, 6.5 g of serine, 0.4 g of tyrosine, and 1 g oftaurine. The solution processor 2306 is configured to adjust theseamounts based on the adjusted amount of amino acid. For example, if theamount of amino acids is increased by 10%, then each of the componentsmay be increased by 10%. Similarly for lipids, the routine or algorithmmay specify that to generate 17 g/l of lipids, a solution should beprepared containing 60 g of soya oil, 60 g of MCTs, 50 g of olive oil,and 30 g of fish oil. The solution processor 2306 may be configured toadjust these amounts based on the adjusted amount of lipids.

In addition, the solution processor 2306 may determine amounts ofmicronutrients to include with the nutritional solution. For instance,the solution processor 2306 may determine an amount of vitamins, traceelements, electrolytes, and/or dipeptides. Vitamins can include, forexample, vitamin A, vitamin B1, vitamin B2, vitamin B3, vitamin B5,vitamin B6, vitamin B7, vitamin B9, vitamin B12, vitamin C, vitamin D,vitamin E, and vitamin K. Trace elements include, for example, chromium(Cr), cobalt (Co), iodine (I), iron (Fe), copper (Cu), manganese (Mn),molybdenum (Mo), selenium (Se), and zinc (Zn). In some instances, thesolution processor 2306 may determine the amount based on a patient'ssex and age, which are correlated to a daily recommended amount of thetrace elements and/or vitamins. In some embodiments, the dailyrecommended amount may constitute a baseline. In these embodiments, thesolution processor 2306 is configured to determine an adjustment to thebaseline using, for example, equations similar to equations (3) and (4)above. In other examples, the solution processor 2306 may simply double(or apply some other factor to) the recommended amount.

Regarding electrolytes, the example solution processor 2306 isconfigured to determine types and amounts based on equations similar toequations (3) and (4) above. For instance, an algorithm or routine usedby the solution processor 2306 may specify that per 1000 ml of solutionto be administered, the electrolytes should include approximately 32.8mmol of sodium to approximately 48 mmol of sodium, approximately 24 mmolof potassium to approximately 36 mmol of potassium, approximately 4.1mmol of magnesium to approximately 6.1 mmol of magnesium, approximately2 mmol of calcium to approximately 3 mmol of calcium, approximately 8.2mmol of phosphate to approximately 15.6 mmol of phosphate, approximately0.032 mmol of zinc to approximately 0.048 mmol of zinc, approximately4.1 mmol of sulphate to approximately 6.1 mmol of sulphate,approximately 28.8 mmol of chloride to approximately 43.2 mmol ofchloride, and approximately 84.8 mmol of acetate to approximately 127.2mmol of acetate.

Regarding dipeptides, the example solution processor 2306 is configuredto determine types and amounts of dipeptides based on equations similarto equations (3) and (4) above. For instance, an algorithm or routineused by the solution processor 2306 may specify that per 1 ml ofsolution to be administered, the dipeptides should include 0.01 g to0.04 g of dipeptides.

The example solution processor 2306 of FIG. 23 may determine that thenutritional solution is to be prepared in one or more packets or bags.In some embodiments, the solution processor 2306 may determine that theamino acid, glucose, and lipid components are to be included within thesame bag. In other examples, the solution processor 2306 may determineor recommend that each of the amino acid, glucose, and lipid componentsare to be included in a separate bag. The determination as to whetherthe components are to be separated may be based on a nutritional statusof a patient, where more malnourished patients may be administeredseparate bags.

While the above description pertains to creating a new solution, in someembodiments, the solution processor 2306 may use the muscle qualityand/or quantity data to select a pre-mixed nutritional solution orselect among of group of predefined formulations. In these examples, thesolution processor 2306 determines an ideal amino acid concentrationand/or solution volume. The solution processor 2306 then compares theideal amino acid concentration and/or volume to a database of premix orpredefined solutions. The solution processor 2306 then selects the mostclosely matching premix and/or predefined solution. Further, in someembodiments, the solution processor 2306 may determine a modification orsupplement to apply to the premix or predefined solution. Themodification or supplement is configured to make the premix and/orpredefined solution more closely resemble the ideal amino acid solution.For example, the solution processor 2306 may specify a number andconcentration of components or ingredients to add to a predefinedsolution to increase the amino acid concentration.

The example solution processor 2306 of FIG. 23 may also determine atotal number of days the solution is to be administered or a totalvolume of solution to be administered. In some instances, the solutionprocessor 2306 is configured to use an equation similar to equations (3)and (4) above where the soft tissue peak is used as a basis fordetermining a number of therapy days. In these instances, the constantmay be between 0.25 and 10. Accordingly, more days are added to thetherapy the further a patient's soft tissue peak is from 45 HU (or otherselected radiodensity value). It should be appreciated that in the aboveexamples, the value of ‘45 HU’ was selected as an example referenceradiodensity value. In other examples the reference may be higher orlower (e.g., 40 HU, 38 HU, 35 HU, etc.) based on one or more thresholdsfor malnutrition.

The example solution processor 2306 is configured to store thenutritional solution components to a solution composition record 2307.The record 2307 is transmitted in one or more messages 444 to, forexample, the EMR server 426 and/or the pharmacy preparation system 420.In some instances, the pharmacy preparation system 420 is configured toprepare a nutritional solution based on the compositions specifiedwithin the record 2307. In other instances, the record 2307 may beprovided as a recommendation to a clinician and/or a pharmacist. Inthese instances, the record 2307 may be accepted or modified before thenutritional solution is prepared.

It should be appreciated that the example solution processor 2306 may bemodified or adjusted based on currently available research data and/orexpert consensus/opinions/guidelines. For example, research data maydetermine new optimal carbohydrate/amino acid/fat doses for differentbody compositions in different clinical circumstances. In response, thesolution processor 2306 may be updated by changing variables, constantvalues, and/or equations to reflect the new research data.

d. Administration Processor

The example nutritional analysis engine 442 of FIG. 23 also includes anadministration processor 2308 configured to determine, for example, pumpparameters for the nutritional therapy pump prescription message 425.The administration processor 2308 may be configured to generate ordetermine the pump parameters based, for example, on a prescriptionreceived from a clinician and/or the composition record 2307 receivedfrom the solution processor 2306. In some instances, the administrationprocessor 2308 may receive an indication from the pharmacy preparationsystem 420 indicative that a specified solution has been prepared. Theindication may also specify component or solution properties that areused by the administration processor 2308 to generate a prescription. Inalternative embodiments, the administration processor 2308 may belocated at the pharmacy preparation system 420 and generate prescriptionparameters for the pump prescription message 425 to program the pump 422in conjunction with a nutritional solution being prepared.

In some embodiments, the administration processor 2308 may also access apatient's EMR for demographic data, physiological values, and/ordisease/condition information. The administration processor 2308 may beconfigured to create pump prescription message 425 to enable the pump422 to be automatically programmed. Accordingly, the administrationprocessor 2308 may be configured to create one or more HL7 messages, forexample, that specify pump parameter values required to program the pump422 to perform a nutritional therapy.

The pump prescription message 425 specified by the administrationprocessor 2308 may include, for instance, a patient identifierparameter, a patient weight parameter, a pump identifier parameter, anda date/time parameter for administration. Regarding the date/timeparameter, the prescription may specify a certain duration during a dayfor administration (e.g., two separate six hour periods) The pumpprescription message 425 may also include parameters for a name of thenutritional solution and/or identifier(s) of components within thesolution, such as a concentration of amino acid. The pump prescriptionmessage 425 may further include parameters for a total volume to beinfused and/or a volume to be infused per bag, container, or packet.Further, the pump prescription message 425 may include parameters for aninfusion rate and/or bolus amount. The administration processor 2308 maydetermine the rate parameter by dividing the total solution per day bythe number of minutes in a day or a number of minutes specified for theadministration. The example administration processor 2308 is configuredto structure the above-mentioned parameters into defined fields orlabels within the pump prescription message 425. The example pump 422 isconfigured to search for certain fields or labels to program the valueof the parameters into corresponding operational settings of the pump.In instances where multiple bags or packets are to be used, theadministration processor 2308 may specify a channel or pump for each bagor packet.

e. Example Process to Program a Nutritional Pump Based on a Patient'sNutritional Status

FIG. 25 shows a flow diagram illustrating an example procedure 2500 toprogram a nutritional infusion pump 422 based on a patient's nutritionalstatus determined by the soft tissue engine 440 of FIG. 6, according toan example embodiment of the present disclosure. The example procedure2500 may be carried out by, for example, the nutritional analysis engine442 of the analysis server 408, as described in conjunction with FIGS.4, 5, and 22 to 24. Although the procedure 2500 is described withreference to the flow diagram illustrated in FIG. 25, it should beappreciated that many other methods of performing the functionsassociated with the procedure 2500 may be used. For example, the orderof many of the blocks may be changed, certain blocks may be combinedwith other blocks, and many of the blocks described are optional.

The procedure 2500 begins when the nutritional analysis engine 442receives one or more messages 414 that include a nutritional statusrecord 618 and/or data distribution data 612 (block 2502). Thenutritional analysis engine 442 determines if one (or more) alarm oralert is to be generated (block 2504). For instance, as described inconjunction with the alarm-alert generator 2304 of FIG. 23, theinformation in the nutritional status record 618 and/or datadistribution data 612 is analyzed or compared to one or morepredetermined thresholds and/or ranges. If a patient's nutritionalstatus indicates that a patient is healthy (e.g., the patient hassufficient muscle quality and/or quantity), the nutritional analysisengine 442 determines that an alarm or alert is not needed. At thispoint, the nutritional analysis engine 442 refrains from generating analarm or alert and returns to block 2502 to receive a nutritional statusfor another patient.

However, if the nutritional analysis engine 442 determines that an alarmor an alert is to be generated, the nutritional analysis engine 442creates the alarm and/or alert and transmits one (or more) message(s)430 indicative of the alarm and/or alert (block 2506). The alarm and/oralert message 430 may identify the patient and the nutritional status ofthe patient. The alarm and/or alert may include a link or field thatenables a clinician to respond with an override, an indication that aprescription is to be prepared, and/or prescription order information.The example nutritional analysis engine 442 next determines if anutrition prescription order has been received from a clinician device424 and/or the EMR server 426. For instance, a clinician, upon receivingan alert or alarm, may create a prescription order for a nutritionaltherapy. The prescription order may be used by the pharmacy preparationsystem 420 to prepare a nutritional solution. The prescription order mayalso be used to program one of the pumps 422.

If a prescription order is provided, the nutritional analysis engine 442creates a pharmacy order providing instructions to the pharmacypreparation system 420 for preparing a nutritional solution (block2510). This includes creating a solution composition record 2307 used toprogram a compounding system within a pharmacy preparation system 420.In some instances, the nutritional analysis engine 442 may determinespecific components for the solution based on the prescription. Forinstance, a prescription may specify that a patient is to receive fourdays of an amino acid enhanced parenteral nutritional solution. Theprescription may also indicate, for example, that 2200 ml of thesolution is to be administered per day and include, for example, 55 g/lof amino acid. The nutritional analysis engine 442 creates the record2307 based on the prescription by identifying specific components oringredients, such as proline, in addition to concentrations or amountsof the components that are to be part of the solution. The nutritionalanalysis engine 442 stores the components and amounts to the record 2307for transmission to the pharmacy preparation system 420.

If a prescription order is not provided, the nutritional analysis engine442 determines a nutritional solution from scratch according to one ormore algorithms or routines (block 2511). The example nutritionalanalysis engine 442 uses a patient's nutritional status, soft tissuepeak, and/or soft tissue peak information to determine a volume ofsolution to be administered per day (or specified time period). Theexample nutritional analysis engine 442 may also determine an amino acidconcentration, lipid concentration, and/or glucose concentration.Further, the nutritional analysis engine 442 determines micronutrientadditives to incorporate into the solution. In some instances, thenutritional analysis engine 442 may compare a patient's soft tissue peakand/or related information to a population correlated to solutioncompositions. The nutritional analysis engine 442 may select thesolution composition that most closely matches soft tissue peak and/orrelated information of individuals in the population.

The example nutritional analysis engine 442 may also determine valuesfor administration parameters for a nutritional therapy pumpprescription message 425 (block 2512). The parameters include, forexample, an infusion rate, a total volume to be infused, a solution nameand/or identifier, a solution (amino-acid) concentration, a patient nameand/or identifier, a pump name and/or identifier, and/or a patientweight. In some instances, the nutritional analysis engine 442 mayaccess a patient's EMR to determine at least some of the values for thenutritional therapy pump prescription message 425. For example, apatient's EMR may include patient information in addition to anidentifier of the pump 422 that will administer the solution to thepatient. In other instances, the nutritional analysis engine 442 leavesthe pump identifier field blank. This field may be determined by the EMRserver 426 after it receives an identifier from a barcode scannerreading an electric or printed barcode on a pump and on a patient. Thenutritional analysis engine 442 may also use information within record2307 for determining values for the administration parameters. Forexample, infusion rate, solution name, and solution concentration may bedetermined from the record 2307.

After determining values for the administration parameters, thenutritional analysis engine 442 creates a nutritional therapy pumpprescription message 425 for the infusion pump 422 (block 2514). Thismay include structuring the parameter values within an HL7-compliantmessage. The value for each parameter may be stored with an appropriatelabel or field within the message 425. The nutritional analysis engine442 transmits the nutritional therapy pump prescription message 425 to,for example, the infusion pump 422 (or the EMR server 426 for routing tothe infusion pump 422). The infusion pump 422 identifies the parametervalues based on labels and/or fields and populates an application orroutine with the parameter values. The infusion pump 422 may thenadminister the nutritional therapy and operate based on the specifiedparameters. At this point, the example procedure 2500 returns to block2502 for the next patient.

CONCLUSION

It will be appreciated that all of the disclosed methods and proceduresdescribed herein may be implemented using one or more computer programsor components. These components may be provided as a series of computerinstructions on any conventional computer-readable medium, includingRAM, ROM, flash memory, magnetic or optical disks, optical memory, orother storage media. The instructions may be configured to be executedby a processor, which when executing the series of computer instructionsperforms or facilitates the performance of all or part of the disclosedmethods and procedures.

It should be understood that various changes and modifications to theexample embodiments described herein will be apparent to those skilledin the art. Such changes and modifications can be made without departingfrom the spirit and scope of the present subject matter and withoutdiminishing its intended advantages. It is therefore intended that suchchanges and modifications be covered by the appended claims.

It should be appreciated that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112,paragraph 6 is not intended to be invoked unless the terms “means” or“step” are explicitly recited in the claims. Accordingly, the claims arenot meant to be limited to the corresponding structure, material, oractions described in the specification or equivalents thereof.

The invention is claimed as follows:
 1. A parenteral nutritionaldiagnostic system comprising: a computed tomography (“CT”) imagingdevice configured to: perform a scan on a mid-section of a patient, andproduce a set of two-dimensional images each of a slice at a differentcross-sectional height of the mid-section, each two-dimensional imageincluding radiodensity data related to imaged tissue of the patient; asoft tissue analysis server communicatively coupled to the CT imagingdevice and configured to select a target two-dimensional image among theset of two-dimensional images by using the radiodensity data todetermine which of the two-dimensional images includes a lowest amountof bone tissue, use the target two-dimensional image to determine atissue surface area for each different value of radiodensity, create adistribution plot of the tissue surface area for each radiodensity valuein Hounsfield Units (“HU”), locate a soft tissue peak within thedistribution plot that corresponds to a local peak in the range of −50HU and 80 HU, and transmit an indication of the soft tissue peak; apharmacy preparation system communicatively coupled to the soft tissueanalysis server and configured to determine a parenteral nutritionaltreatment is to be performed before a medical procedure is to beperformed for the patient if the soft tissue peak is below apredetermined threshold, determine a nutritional order parameter of theparenteral nutritional treatment based at least in part on the softtissue peak, and transmit the nutritional order parameter of theparenteral nutritional treatment; and a parenteral nutrition pumpcommunicatively coupled to the pharmacy preparation system andconfigured to program a parenteral nutrition infusion therapy based onthe received nutritional order parameter of the parenteral nutritionaltreatment, and provide the parenteral nutrition infusion therapy to thepatient.
 2. The system of claim 1, further comprising an electronicmedical records server configured to: receive and store the soft tissuepeak; compare the soft tissue peak to the predetermined threshold;transmit an alarm if the soft tissue peak is below a predeterminedthreshold; and transmit a message to the pharmacy preparation systemindicative that the parenteral nutritional treatment is to be performed.3. The system of claim 1, wherein the nutritional order parameter of theparenteral nutritional treatment includes at least one of a nutritionvolume to be infused, a nutrition solution to be infused, an infusionrate, or an infusion duration.
 4. The system of claim 3, wherein thenutrition solution includes at least one of a carbohydrate, a lipid, aprotein, sodium, potassium, calcium, iron, magnesium, phosphate,acetate, chloride, folic acid, an amino acid, an omega-3 fatty acid, avitamin, and a supplement.
 5. The system of claim 1, wherein thepharmacy preparation system is configured to at least one of: (i)prepare a nutritional substance for the parenteral nutrition infusiontherapy based at least in part on the soft tissue peak; or (ii) select apremix container for the parenteral nutrition infusion therapy based atleast in part on the soft tissue peak.
 6. The system of claim 5, whereinthe pharmacy preparation system is configured to determine thenutritional order parameter and perform at least one of (i) and (ii)based additionally on at least one of an age of the patient, a gender ofthe patient, a weight of the patient, a disease state of the patient, aphysiological parameter of the patient, and the medical procedure to beperformed on the patient.
 7. The system of claim 1, wherein theparenteral nutrition pump includes a large volume pump or agravity-operated pump.
 8. A parenteral nutritional diagnostic apparatusoperable with at least one imaging device comprising: an image interfacecommunicatively coupled to the at least one imaging device, the imageinterface configured to receive a set of medical images, each medicalimage (i) taken of a different cross-section of a patient, and (ii)including radiodensity data related to imaged tissue of the patient; andat least one processor configured to select a target medical image amongthe set of medical images that corresponds to a desired area of thepatient, use the target medical image to determine a tissue area foreach different value of radiodensity, determine a distribution of thetissue surface area for each radiodensity value, locate a soft tissuepeak within the distribution that corresponds to a local peak at aregion in the distribution that is related to muscle tissue, organtissue, and intramuscular adipose tissue, and provide soft tissue peakinformation to evaluate a nutritional status of the patient.
 9. Theapparatus of claim 8, wherein the different cross-sections are differentlateral cross-sections of a mid-section of the patient.
 10. Theapparatus of claim 8, wherein the desired area of the patient is betweena third lumbar vertebra and a fourth lumbar vertebra.
 11. The apparatusof claim 8, wherein the at least one processor is further configured to:compare the nutritional status of the patient to a predeterminedthreshold; transmit a message indicative that a nutritional treatmentbefore another medical procedure is not needed if the nutritional statusis greater than the predetermined threshold; and transmit a messageindicative the patient should undergo the nutritional treatment if thenutritional status is less than the predetermined threshold.
 12. Theapparatus of claim 8, wherein the at least one processor is furtherconfigured to determine the tissue area for each different value ofradiodensity by: creating a number of bins between −150 HU and 150 HU,each bin corresponding to a different value of radiodensity; assigningeach pixel from the target medical image to one of the bins; anddetermining a tissue area for each bin by summing the pixels assigned tothe respective bin.
 13. The apparatus of claim 12, wherein the bins havea width between 0.1 HU and 2 HU.
 14. The apparatus of claim 12, whereineach of the pixels within the target medical image is color-coded basedon the radiodensity of the tissue shown within the pixel.
 15. Theapparatus of claim 8, wherein the at least one processor is furtherconfigured to: determine a center-of-mass within the target medicalimage; impose a region-of-interest over the target medical image suchthat a geometric center of the region of interest is aligned with thecenter-of-mass; and determine a first tissue area for each differentvalue of radiodensity of the target medical image that corresponds tothe region-of-interest.
 16. The apparatus of claim 15, wherein thecenter-of-mass is a first center-of-mass, and the region-of-interest isa first region-of-interest, and wherein the at least one processor isconfigured to: identify bone tissue within the first region-of-interest;determine a second center-of-mass within the first region-of-interestusing only the bone tissue; impose a second region-of-interest toreplace the first region-of-interest over the target medical image suchthat a geometric center of the second region of interest is aligned withthe second center-of-mass; and determine a second tissue area for eachdifferent value of radiodensity of the target medical image thatcorresponds to the second region-of-interest.
 17. A parenteralnutritional diagnostic method comprising: acquiring in at least oneprocessor from an imaging device, a set of medical images each of aslice at a different cross-section of a patient, each image includingradiodensity data related to imaged tissue of the patient; analyzing,via the at least one processor, the set of medical images to determine atotal bone tissue area that corresponds to a designated area of thepatient; selecting, via the at least one processor, a target medicalimage from the set of analyzed medical images that has a lowest totalbone tissue area; determining, via the at least one processor, from thetarget medical image a distribution of radiodensity values; identifying,via the at least one processor, a local peak at a region in thedistribution of radiodensity values that is related to at least one ofmuscle tissue, organ tissue, or intramuscular adipose tissue;evaluating, via the at least one processor, a first nutritional statusof the patient if a radiodensity value of the soft tissue peak is withina first radiodensity range; and evaluating, via the at least oneprocessor, a second nutritional status of the patient if a radiodensityvalue of the soft tissue peak is within a second radiodensity range. 18.The method of claim 17, further comprising smoothing the distribution ofradiodensity values using at least one of a Savitzky-Golay digitalfilter, a moving-average filter, a multipass filter, or a convolutionfilter.
 19. The method of claim 17, wherein the distribution ofradiodensity values includes at least one of (i) a number of pixels ofthe target medical image for each radiodensity value, or (ii) a tissuesurface area of tissue provided in the target medical image for eachradiodensity value.
 20. The method of claim 17, wherein the firstradiodensity range is from 40 HU to 80 HU, and wherein the secondradiodensity range is from 0 HU to 40 HU.
 21. The method of claim 17,further comprising: determining, via the at least one processor, that atleast one of an alarm or an alert is to be generated based on thepatient having the second nutritional status; and transmitting, via theat least one processor, the at least one of the alarm or the alert to atleast one of a clinician device, a pharmacy preparation system, and anelectronic medical record server.
 22. The method of claim 17, furthercomprising: determining, via the at least one processor, (i) a componentor composition of a nutritional solution, and (ii) a total volume of thenutritional solution to be administered based on the patient having thesecond nutritional status and at least one demographic trait of thepatient; and providing, via the at least one processor, (i) and (ii) toa pharmacy preparation system to prepare the nutritional solution. 23.The method of claim 22, further comprising: determining, via the atleast one processor, administration parameters for an infusion pumpbased on (i) and (ii); creating, via the at least one processor, anadministration message that includes the administration parameters andan identifier of the patient; and transmitting, via the at least oneprocessor, the administration message to the infusion pump to cause anutritional therapy to be administered to the patient identified by theidentifier and according to the administration parameters.